text:
Did you know Garang Boys are problematic in South Sudan Politics?
Opinion Peter Gai
by . July 14, 2015 8 2
By Peter Gai Manyuon,
SPLM_Uhuru, the former political detainees, who were released into custody of the Kenyan president, Uhuru Kenyatta are holding onto another loose coalition that may never last. The detainees allied with Dr. Machar, which led to their arrest but afterwards claim to be "pure" to stand alone(Photo: AP/Kenyan presidency)
July 14, 2015(Nyamilepedia) — Transitional Constitution of South Sudan 2011 was manipulated by Garang Boys. South Sudan resources looted by the boys and the current crisis was a strategic work plans for five years by (Yaal Garang) with their mother.
Are you aware that these boys are the problem to South Sudanese communities? Are you aware that, they are looking for South Sudan leadership only that their mechanism is not effective? Are you informed that, out of 75 corrupts officials, 50 are within them? Are you informed that, they bought houses in East Africa region using Country resources? Are you informed that, they try by all means to lead the interim government but they failed politically? Did you discover that, half of them never when to any formal school? Do you know that most of them studied through distance education from South Africa? Did you know that, some of them are not yet acknowledge as South Sudanese nationals? Did you know that President Kiir will appoint Speaker of South Sudan Legislative Assembly from the former detainees? Are you informed that, Minister of Interior will be given to one of them? Did you learn that Comrade Telar Ring and Aleu were pushed out from Cabinet due to request from former detainees? Do you know that Taban Deang Gai and Malong Awan are having underground coup to remove Kiir from power for them to lead South Sudanese, where Malong will be President for six months and Taban vice President for six months and there after elections? Have you heard of Comrade Taban lobbying for over throw of Dr Machar from SPLM in opposition leadership? And are you aware that Comrade Pagan Amum was misinformed that Kiir will die in the next three months according to special doctor report, so that he can take the leadership using SPLM protocol since Comrade Wani Igga is a Liability and Dr Machar is in rebellion side?
Apparently, it's well understood that most of the Sudan People Liberation Movement (SPLM) members are all thieves. Those in the government, opposition and the remaining three within the former detainees group, they are all liabilities to South Sudanese communities. South Sudanese must think of how to isolate them all for the peaceful co-existence of South Sudanese masses to be achieved or put in to place. Isolation of the seventy five (75) corrupts individuals who have incited South Sudanese people to killed themselves on ethnic basis is what most people should be advocating for.
It should be from each community to isolate their person from participating in the politics of the nation. Don't ask me why?
Nevertheless, since the inception of Comprehensive Peace Agreement (CPA) in 2005, Referendum and the declaration of the Independence of South Sudan as the Sovereign State on the 9th of July 2011, most of the "Garang Boys" were the one stealing the resources of the Country where they eventually took billions of dollars to foreign Countries for their own investments and benefits.
Absolutely, according to the research I carried out with the help of International experts in East Africa, most of the 75 corrupts political sycophants, 60% out of 75 officials are all Garang Boys. They have build/bought houses in the entire East Africa region due to the resources of the Country that they have looted. Very disgraceful indeed, South Sudanese are now known internationally, regionally as thieves of their own properties!
The dissolution of the entire cabinet by President Kiir Mayardit in July 2013, most of the Garang boys/ orphans ganged themselves and blindfold Dr Riek Machar for the support that was not accomplish for (Dr. Machar) to be the flag bearer of the SPLM in 2015 general elections that was suppose to take place early this year. Dr Riek was not aware that the "boys" he is dealing with are all thieves who are experts in destroying his ideology of becoming the President of South Sudan. Machar was not informed about the vision and the mission of the group. Very interesting politics of 21th century in South Sudan!
Suddenly, after they realized that Kiir Mayardit and Riek Machar were not of the view to have a common goal of signing a Transitional Government of National Unity (TGONU) where (Garang Boys) presumed to be part and parcel of the peace initiatives, they eventually formed a collusion called "former G10" that is integrated in to the SPLM-in Government and other three (3) remained isolated and now are very confused of where to go.
Apparently, these great thieves of late Dr. John Garang are the problem to South Sudanese people. It is the time for South Sudanese communities to re-think what mechanism to isolate them politically.
Obviously, the combination of these crass individuals known as "Garang Boys" have lead to the loss of lives and properties among the great communities of Upper Nile region likewise to South Sudanese in general.
The methodology of manipulation is the ideology of Garang Boys and their mother Rebecca Nyadeang De Mabior. Rebecca Nyadeang should know that, the legacy of Late Dr. John is spoiled already by her children which consists of John Luke, Majak De Agok, Taban Deang Gai, Gier Chuong, Deng Alor, Oyai Deng, Gathoth Mai, Pieng Deng, Kuol Manyang, and the South Sudanese who is leading in corruption Comrade Pagan Amum as well nickname as Elder Son of Late Dr. John.
Practically, the current crisis was initiated by the comrades within the SPLM as the ruling party of South Sudan in this 21th century. Taking the people of South Sudan to zero level was a strategy from these bums of hooligans known by the name former G10 or detainees. It is well acknowledged that all Late Garang Children known as" Yaal Garang or Nyadeang" are the one that created the confusion between General Salva Kiir and Dr Riek Machar.
It is well understood that, most of these politicians were the one behind the scene in the newest nation. After they failed from lobbying for the positions from International Community to lead South Sudanese in the Interim period, they ended up divided themselves and some joined SPLM in the government and some ended up in the middle and now they don't know where to go. What a disgrace?
More so, in 2012 May, most of "Yaal Garang" plot a coup against President Kiir and Machar as well in 2013 June where they finally disagreed among themselves on who to lead the people of South Sudan in case they succeed in their plot. What lead to their disagreement was the issue of who to lead if they accomplish their mission of killings the two gentlemen.
Even God almighty have realized that, the combination of Pagan Amum, Taban Deang Gai, Deng Alor, John Luke, Majak De Agot and their mother Nyadeang Garang is a big shame to South Sudanese people globally.
God almighty who created these useless politicians in South Sudan territory should re-think of what could reconcile most of the civil population at this particular period of time.
These categories of people were the one who manipulated the Transitional Constitution of South Sudan 2011 and billions of dollars got disappeared in their hands.
These boys have one ideology of ruling the people of South Sudan but the only issue is that most of them lack support from their communities across the Country. Due to disagreement between Dr Majak Agot and Comrade Taban Deang Gai lead to their movement separately up to this movement , where they divided themselves in to SPLM in government, SPLM in opposition and SPLM former Detainees that eventually joined genocidal government in Juba.
Hence, the orphans of Late Dr John Garang are seriously looking for leadership day and night in South Sudan. The problem is that no one is supporting them from the citizen of South Sudan, due to their background of corruption cases, chronic tribalism and illiteracy. Even thought they managed to confused Dr Riek Machar and General Salva Kiir Mayardit to clash among themselves in 2013, this time around they will never manipulate any one again in the Country because most of the citizens have eventually known them properly.
None of the former G10 or detainees does not deserve leadership in South Sudan due to their background from their respective constituencies across the Country.
The Author is an Independent Journalist and Columnist who has written extensively on issues of Democratization and Human Rights in South Sudan. He could be reach on independentjournalistpgm@gmail.com.
The statements, comments, or opinions published by Nyamilepedia are solely those of their respective authors, which do not necessarily represent the views held by the moderators of Nyamilepedia. The veracity of any claims made are the responsibility of the writer(s), and not the staff and the management of Nyamilepedia.
Nyamilepdeia reserves the right to moderate, publish or delete a post without warning or consultation with the author(s). To publish your article, contact our editorial team at nyamilepedia@gmail.com
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288weechwang July 14, 2015 at 7:44 am
It was well planed by so called garang's boys. And i remember when Majak Agot framed General Gatwech Dual, The current Chief General of staff for SPLA/M-IO and jailed till his released in 2013 prior to crisis. Yet, he was the mastermind of an incident of that year. No one said anything of why many General from Nuer ethnic were relieved Majak Agot attempted coup. But it went quite as if nothing had occurred. There was not even an investigation launched after and during the incident. Majak got away with it. Most people suspected Majak for that incident. Majak was near missed.
Ayuen Mading July 14, 2015 at 8:45 pm
Majak Agot did not dream for a coup in Republic of South Sudan, it was propaganda make by enemies of Majak, particular those group of Unfortunate Dr Riek Machar, in order for him to explode the chance as to divert the attention of people from him toward Dr Majak, and evident is true when Riek made coordinate coup with only nuer who are in SPLA rank and file through out the country, so Dr Majak was deform by his enemies in security organ, how can Majak make a coup to some one who build him
tutu July 14, 2015 at 9:24 pm
The so call Garang's boys aren't the problem anymore, we neutralized them, what they don't want to accept is the death of their father. the power has shifted since that time, Upper Nile is our number one enemy, they will not get power what so eve. Equatorial are trying their best to sabotage our government but we have plans for them, wait and see.
Semei Kakunggulu July 14, 2015 at 8:41 am
thank, good read nonetheless. iam also confused by everything going in our country.
monyjiangadit July 15, 2015 at 12:48 am
Nothing like Garang Boys it is defomation of others by those who wanted to grap power, Garang Did not bring anyone from nowhere it was citizen of South Sudan who where loving his ideas and become comradeship
AGUMUT July 14, 2015 at 11:32 pm
I had live with Chol Tong for almost two years. Very intelligent,but i don't know why he puts himself with those wild policians and Doorsteps politics to destroy his own career. South Sudan need qualified engineers.
Very Wild Politicians and no good because they got big money,but they can't chew those money once.
monyjiangadit July 15, 2015 at 5:15 am
Mr. Agumut do not blame Engineer Chol Tong of being among politician, because those who practice politic did not study political science, President Museveni once said anyone who went to University to study political science is doing nothing, because politic is art of practicing for winning people heart, so even Medicat Doctors, Engineer, Architecture, economist, and so on are become the best politician, so mind you that politic did not origin some where but ability to win people vote,


As those who've followed us on Facebook for a while will know, our 4 hens were massacred in July 2014 by 'free range' local dogs. Although the hens were kept in a secure compound which not even the foxes had managed to get into, these dogs succeeded in opening the fastening on the gate, broke it down and got in. I found the bodies of two of the hens. The other two were taken. They were only 2½ years old and at the peak of their laying. It was a sad loss.
It was all the more upsetting considering the effort put into building a really secure compound for them. I'd catered for large 'free range' dogs in building the compound, but not ones with door-opening skills. This forced a major rethink on how I was to keep and protect poultry going forward. It came back again to the initial conundrum I'd faced.
Tractoring poultry isn't the easiest thing to do on these narrow terraces and uneven, sloping ground. Any tractor-type structure light enough to be hauled 3-5m from one terrace up to the next by one person doesn't provide much protection against determined predators.
The idea had originally been to base the hens in a permanent compound, but tractor them about the place from time to time during the day. I built a geodesic dome chicken tractor which was very light and easy to move, but awkward to get the chickens out of, needed resiting too often because of its small size, and couldn't be used around trees. So the tractoring never really happened in the way it was intended.
The compound though was a huge concession to safety over quality of life, and also meant I wasn't employing the hens on the quinta in the way I wanted to, using them to clear pests like codling moth larvae under the fruit trees in the winter, or prepare new ground for cultivation. And any permanent compound suffers from overuse sooner or later.
Clearly I needed a better kind of tractoring system.
I also want to add ducks to the flock. Ducks would need separate housing to the hens. Ducks would also need access to water.
So the plan is again to have a permanent base for the poultry on the bottom terrace, complete with duck and hen housing and duck ponds, but to use electric poultry netting to contain them. In this way, the electric fencing is both flexible fencing for the compound and a tractor to move round the quinta so I can put them to work. Whether they return to the permanent coops every night and the fencing is moved back with them to deter foxes and the like, or whether I construct some portable housing for them which gets moved around with the fencing remains to be seen. It's a work in progress.
The electric fencing also won't protect the birds from sky-based predators, but I came to the conclusion there's only so much protection I can offer before it becomes completely uneconomical. Instead I will maintain a stock of birds which takes predation into account.
The first thing to do though was to make accommodation and ponds for the ducks in the area designated their permanent base.


In the beginning of December a Hydrex diving team mobilized to a 170-meter container vessel with one of our workboats. The team installed a doubler plate over the cavitated area of the rudder during the ship's scheduled maintenance stop in Rotterdam.
An earlier inspection carried out in Algeciras had revealed the damage to the rudder. A condition of class was imposed on the vessel and a custom solution was needed. We proposed a repair plan to the class that would allow the vessel to keep sailing until the next scheduled docking.
Deployment to the vessel was done using a Hydrex workboat loaded with all the needed equipment. The catamarans are fully equipped as dive support stations with hydraulic cranes, winches and a dive control room. They can be used for a wide range of operations in Belgium, the Netherlands and France. This increases flexibility of operations.
After our team arrived on-site with the workboat, they performed an underwater inspection of the leading edge of the rudder, where the damage was situated. The divers then started preparing the affected area for the installation of the doubler plate.
The plate had been prepared in advance. This was done with the information of the preliminary inspection in Algeciras combined with the drawings of the rudder, provided by the owner.
When the rudder had been prepared our certified diver/welders fitted the plate and secured it. The team then installed anodes on both sides of the rudder for further protection. This concluded the repair.
A dive support workboat allowed for fast mobilization to the ship's location in Rotterdam. The diver/technician team worked in shifts and finished the operation in 24 hours within the schedule of the container vessel.
During the operation a representative of the classification society was present. He gave his approval for the repair. The owner can now sail his ship until the next scheduled drydocking without having to worry about further unscheduled delays or repairs to its rudder.


text:
How Live Nation and Ticketmaster have a monopoly on the live entertainment industry
The Senate Judiciary Committee held a listening to this week titled, "That is the Ticket: Selling Competitors and Defending Shoppers in Stay Leisure," which targeted on the state of Stay Nation Leisure and the dearth of competitors within the main and secondary ticketing markets.
"I simply need to dispel this notion that this isn't a monopoly after which we will go from there about options," Sen. Amy Klobuchar, D-Minn., mentioned on the listening to, which was held Tuesday.
Live Nation Entertainment
consists of Stay Nation, an occasions promoter and venue operator, and Ticketmaster, a ticket gross sales large. The 2 corporations merged in 2010 and now management an estimated 70% of the ticketing and live event venues market.
It is no secret that Taylor Swift followers have been outraged in November 2022 when hundreds of thousands flocked to Ticketmaster.com to seize tickets to see the heartbreak queen for the primary time since 2018 and the web site crashed. The lengthy wait traces and frozen screens sparked an uproar with followers, blaming Ticketmaster for ruining their possibilities to see the pop star.
"Because the main participant, now we have an obligation to do higher," mentioned Joe Berchtold, Stay Nation Leisure president and chief monetary officer, on the listening to Tuesday.
This isn't the primary time shoppers have referred to as for the breakup of Ticketmaster and Stay Nation. It is also not the primary time the Division of Justice has been reportedly trying into alleged misconduct by the corporate.
When the Stay Nation and Ticketmaster merger was authorised in 2010, it was below the situation of a consent decree. Amongst different issues, the aim of that settlement was to forbid Stay Nation from retaliating towards a venue for utilizing a ticketer aside from Ticketmaster. After an investigation, in 2019 the DOJ made its most important enforcement motion of an antitrust decree in 20 years when it alleged Stay Nation Leisure violated that decree. The company settled with the federal government.
"The Division of Justice alleged six points in 2019 which led to our resolution with them to increase the consent decree. We didn't really feel it made sense to be seen as defending the theories of retaliation or threats. It is not our enterprise follow. It goes towards our basic concentrate on alignment with the artists. The concept that we might ever put our pursuits forward of theirs. So we're snug extending the consent decree," mentioned Berchtold throughout Tuesday's listening to. "It's completely our coverage to not stress, threaten or retaliate towards venues through the use of content material as a part of the ticketing dialogue," he added.
In November 2022, The New York Times reported the DOJ is as soon as once more investigating the corporate.
Whereas Stay Nation Leisure arguably has a monopoly on the business, a monopoly in itself just isn't unlawful in the USA. A monopoly happens when an organization holds exclusive possession or control of an business.
"If we made monopolies unlawful on the premise of pricing above value and producing monopoly earnings for a agency, the priority can be that that might probably stifle risk-taking and entrepreneurial exercise," mentioned Diana Moss, president of the American Antitrust Institute.
Abuse of a monopoly place is one other matter. It is unlawful for a enterprise to determine or keep a monopoly by way of improper conduct and never enable for others to enter the market.
Clyde Lawrence, a singer-songwriter within the New York Metropolis-based band Lawrence, testified throughout Tuesday's listening to. The band frequently interacts with Stay Nation Leisure. It's typically their promoter, venue operator and ticketer.
"In a world the place the promoter and the venue will not be affiliated with one another, we will belief that the promoter will look to get the perfect deal from the venue; nevertheless, on this case the promoter and the venue are a part of the identical company entity so the road objects are basically Stay Nation negotiating to pay itself," Lawrence mentioned.
The band instructed CNBC in the event that they need to play a sure measurement venue in a selected metropolis, they're typically left no selection aside from to make use of Stay Nation due to the dearth of competitors in some areas. Then in the event that they want to use one other ticketer aside from Ticketmaster, they are saying that isn't an possibility.
"Ticketmaster has created these unique contracts, when you signal that contract, a band just isn't allowed to come back in and say, 'we need to promote our tickets with X, Y, Z platform,'" mentioned Jordan Cohen, one of many band's eight members.
They even have a song with the lyric, "Stay Nation is a monopoly." "Attributable to Stay Nation's management throughout the business, now we have virtually no leverage in negotiating," Lawrence mentioned.
Whereas the corporate does have some competitors, consultants say no different agency presently stands an opportunity.
"There's actually nobody that is been in a position to get the kind of scale that Stay Nation has. The closest comparable is Anschutz Leisure Group with their very own sort of inner ticketing platform. However they made a press release that speaks to the market energy of Ticketmaster, which is that they used Ticketmaster to ticket Taylor Swift," mentioned Barton Crockett, managing director and senior fairness analyst at Rosenblatt Securities.
It is a enterprise that lots of people have checked out. They've spoken about eager to get into it, and nobody's actually been in a position to seize sufficient market share to essentially be a significant participant," he added.
Stay Nation declined CNBC's request for an interview or remark however in a statement on its website said that it's against company policy to threaten venues if they do not use Ticketmaster and that it doesn't retaliate for a misplaced ticketing deal.
It's unclear what's subsequent for Stay Nation Leisure.
Watch this video to be taught extra about how the corporate obtained to the place it's right now and what the long run would possibly maintain.
Categories Top Treading Tags Entertainment, industry, live, monopoly, nation, Ticketmaster
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redpajama_set_name: RedPajamaCommonCrawl


THE billionaire Reuben brothers are poised to snap up Ayr racecourse, home to the Scottish Grand National, in a £12.5m deal. It is understood the brothers, who will add the course to their Northern Racing empire, are just days away from announcing a deal with owners Richard Johnstone and Alan Macdonald.
Scotland's premier racecourse was put up for sale earlier this year. The deal is thought to include the Western House hotel next to the course. The six-furlong Ayr Gold Cup, run in September, is one of the most competitive sprints in the Flat Racing calendar, attracting large crowds.
The Reubens — who made their fortune from property and metals trading — agreed a £66m takeover of Northern Racing, owner of Chepstow, Uttoxeter and Bath racecourses, two-and-a-half years ago. They also have a 26% stake in Arena Leisure, owner of the oldest classic race in the world, the St Leger.


text:
\section{Introduction}
The purpose of online action detection is to correctly identify ongoing actions from streaming videos without any access to the future.
Recently, this task has received increasing attention due to its great potential of diverse application prospects in real life, such as autonomous driving~\cite{self-driving}, video surveillance~\cite{surveillance}, anomaly detection~\cite{anomaly,anomaly_2}, \etc.
The crucial challenge of this task is that we need to detect the actions at the moment that video frames arrive with inadequate observations.
To solve the problem, it is important to learn the long-range temporal dependencies.

\begin{comment}
Online action detection task aims to correctly identify ongoing actions from streaming videos without any access to the future.
Recently, this task has received increasing attention due to its great potential of diverse application prospects in real life, such as autonomous driving~\cite{self-driving}, video surveillance~\cite{surveillance}, anomaly detection~\cite{anomaly,anomaly_2}, \etc.

However, different from the recently extensively studied temporal action detection~\cite{scnn,SSN,RC3D,BSN,PGCN,BMN,GTAD,BCGNN} and action recognition~\cite{TSN,I3D,tsm,slowfast,TPN} that can utilize the observed information of the whole video in an offline manner, online action detection needs to detect the actions at the moment that video frames arrive with inadequate observations.
Since it is difficult to model long-range temporal dependencies and only part of the action can be available (\ie, future information is not available), online action detection is more challenging and worthy of further research efforts.
\end{comment}

\begin{figure}[t]
\centering{\includegraphics[width=1.0\linewidth]{pictures/loss_compare.pdf}}
\vspace{-0.7cm}
\caption{\label{motivation}
    %
    Comparison between OadTR and the state-of-the-art online action detection methods (\ie, TRN~\cite{TRN} and IDN~\cite{IDN}):
    %
    (a) Comparison of the training speeds;
    %
    (b) Comparison of the inference speeds;
    %
    (c) Comparison of the performance on the challenge THUMOS14 dataset.
}
\vspace{-0.4cm}
\end{figure}

Current approaches tend to apply RNN to model the temporal dependencies and have achieved impressive improvements~\cite{donahue2015long,li2016online,RED,WACV,TRN,IDN}.
Typically, Information Discrimination Network (IDN)~\cite{IDN} designs a RNN like architecture to encode long-term historical information, and then conduct action recognition at current moment.
However, RNN like architectures have the problems of non-parallelism and gradient vanishing~\cite{pascanu2013difficulty,critical_RNN}.
Thus it is hard to optimize the architectures, which may result in an unsatisfactory performance.
This is a challenging problem for current approaches, but much less effort has been paid to solve it.
To further improve the performance, we need to design a new efficient and easily-optimized framework.
For this purpose, we propose to apply Transformers~\cite{Transformer}.
Transformers possess the strong power of long-range temporal modeling by the self-attention module, and have achieved remarkable performance in both natural language processing~\cite{Transformer,bert,Big_bird} and various vision tasks~\cite{ViT,DETR_1}.
Existing works have proved that Transformers have a better convergence than RNN architectures~\cite{transformer-vs-rnn-convergence, transformer-vs-rnn-fast}, and they are also computationally efficient.
The above properties of Transformers can naturally provide alternative scheme for the online action detection task.

\begin{comment}
To overcome aforementional limitations, extensive efforts have been devoted and most existing state-of-the-art approaches~\cite{donahue2015long,li2016online,RED,WACV,TRN,IDN} are based on Recurrent Neural Networks (RNNs).
These methods apply recurrent neural networks (\eg, LSTM~\cite{LSTM} and GRU~\cite{GRU}) to learn long-term temporal relationships.
However, they suffers from the inherent problems of recurrent neural networks, \ie, non-parallelism and vanishing gradient~\cite{critical_RNN}.
The above problems cause the model to be inefficient and difficult to converge during training, which in turn leads to limited ability to capture long-range dependencies and limited performance improvements.
Therefore, how to efficiently exploit the relevance between input frame chunks remains an open and challenging problem.
Inspired by the Transformer's tremendous success in natural language processing~\cite{Transformer,bert,Big_bird}, recently, many methods~\cite{DETR,ViT,DETR_1,DETR_2} have introduced Transformer to various vision tasks, efficiently capture the dependencies between each patch and show strong performances.
Our insight is that Transformer parallelly models non-local interactions with multi-head self-attentions to capture global context can solve the existing problems in online action detection task.
\end{comment}

The above observations motivate this work.
In particular, we propose a carefully designed framework, termed OadTR, by introducing the power of Transformers to the online action detection task, as illustrated in Figure~\ref{network_encoder_decoder}.
The proposed OadTR is an encoder-decoder architecture which can simultaneously learn long-range historical relationships and future information to classify current action.
The first step is to extract clip-level feature sequence from a given video by a standard CNN.
We then embed a task token to the clip-level feature sequence and input them to the encoder module.
By this means, the output of the task token can encode the global temporal relationships among the historical observations.
In contrast, the decoder is designed to predict the actions that may take place in the future moments.
Finally, we concatenate the outputs of both the task token and decoder to detect the online actions.
We compare OadTR with other RNN based approaches in Figure~\ref{motivation}, which shows that the proposed OadTR is both efficient and effective.
To further demonstrate the effectiveness of OadTR, we conduct a large number of experiments on three public datasets, including HDD~\cite{HDD}, TVSeries~\cite{online_de}, and THUMOS14~\cite{thumos14}, and achieve significant improvements in terms of both mAP and mcAP metrics.

Summarily, we make the following three contributions:
\begin{itemize}

\item[$\bullet$] To the best of our knowledge, we are the first to bring Transformers into online action detection task and propose a new framework, \emph{i.e.}, OadTR;
\item[$\bullet$] We specially design the encoder and decoder of OadTR which can aggregate long-range historical information and future anticipations to improve online action detection;
\item[$\bullet$] We conduct extensive experiments, and the results demonstrate the proposed OadTR significantly outperforms state-of-the-art methods.
The massive and comprehensive ablation studies can further dissect the undergoing properties of OadTR.
\end{itemize}

\begin{comment}
In this paper, we aim to introduce the power of Transformer to online action detection and propose a new model termed \textit{ONline TRansformer} (ONTR).
Concretely, the encoder augmented with a task token is appled to the feature sequence extracted from the raw video to explore the relationships and global interactions between historical observations.
The decoder is to make up for the unavailability of future information and aims to predict the actions that will occur in the future.
Finally, the historical global information and the predicted future context are aggregated to help classify current actions. As a result, the representation and memorization capabilities of our ONTR is largely enhanced.
In particular, our ONTR converges quickly (Figure~\ref{motivation}(a)) with the Transformer structure (\eg, residual connection~\cite{resnet} and layer norm~\cite{LN}), computational efficiency is improved (Figure~\ref{motivation}(b)), and ultimately surpasses the current state-of-the-art approaches in terms of performance (Figure~\ref{motivation}(c)).

We evaluate our approach on three widely-used online action detection datasets, namely HDD~\cite{HDD}, TVSeries~\cite{online_de}, THUMOS14~\cite{thumos14}.
Extensive results demonstrated the effectiveness and generalization of our ONTR.
The main contributions are summarized as follows:
\begin{itemize}

\item[$\bullet$] To the best of our knowledge, we are the first work to introduce Transformer into online action detection task and propose a new network architecture, ONTR, based on Transformer.
\item[$\bullet$] We design an encoder augmented with a task token to capture information and discriminative representations bewteen historical observations,  and a decoder predicts the future information in parallel as a supplement to the current.
\item[$\bullet$] The experimental results demonstrate the proposed ONTR significantly outperforms state-of-the-art methods on three popular benchmark datasets. The massive and comprehensive ablation studies further verifies the high effectiveness for online action detection.
\end{itemize}
\end{comment}

\vspace{-1pt}
\section{Related Work}
\label{sec:related_work}
\vspace{-0pt}
In this section, we will review the related methods of our approach as follows:

\xhdr{Online Action Detection.}
Given a live video stream, online action detection aims to identify the actions that are taking place, even if only part of the actions can be observed.
De Geest~\etal~\cite{online_de} explicitly introduced online action detection task for the first time and proposed TVSeries dataset.
After that, they also proposed a two-stream feedback network with LSTM~\cite{LSTM} to model temporal structure~\cite{WACV}.
RED~\cite{RED} designs a Reinforced Encoder-Decoder network and a module to encourage making the right decisions as early as possible.
Several recent methods~\cite{Startnet_2,Startnet} focus on detecting action starts and minimizing the time delay of identifying the start point of an action.
IDN~\cite{IDN} directly manipulates the GRU cell~\cite{GRU} to model the relations between past information and an ongoing action.
Inspired by that humans often identify current actions by considering the future~\cite{future}, TRN~\cite{TRN} uses LSTM to predict future information recursively and combine it with past observations to identify actions. In this paper, we also introduce future information to assist in identifying current actions, but in parallel.
Note that the aforementioned methods adopt RNN to model input action sequences, which are inefficient and lack of interaction between features, resulting in poor modeling capabilities for long-term dependence.

\xhdr{Temporal Action Detection.}
The goal of temporal action detection is to locate the start time points and end time points of all action instances in the untrimmed video.
One-stage methods~\cite{SSAD,CTCN} draw on the SSD~\cite{SSD} method in object detection and design end-to-end action detection networks with multi-layer feature pyramid structures.
Two-stage methods~\cite{RC3D, rethinking} adopt the Faster-RCNN~\cite{faster_rcnn} architecture, including proposal generation subnet and proposal classification subnet.
Most recent methods focus on generating high-quality temporal action proposals.
These methods~\cite{BSN,BMN,DBG,GTAD,BCGNN} usually locate temporal boundaries with high probabilities, then combine these boundaries as proposals and retrieve proposals by evaluating the confidence of whether a proposal contains an action within its region.
However, the above approaches assume the entire input video can be observed, which is not available in the online task.

\begin{comment}
\xhdr{Early Action Detection.} Unlike online action detection, early action detection predicts actions from partially observed videos only containing action frames.
Hoai~\etal~\cite{early_1} solves this problem by proposing a max-margin framework with structured SVMs.
This method focus on simple scenarios, \eg, one video contains only one action.
Cai~\etal~\cite{early_2} employs LSTM to model temporal dependencies and designs a ranking loss for training, assuming that the gaps of predicted scores between correct and incorrect actions should be non-decreasing when a model observes more of an action.
\end{comment}

\xhdr{Transformers.} Since the success of the Transformer-based models in the natural language processing field~\cite{Transformer,bert,Big_bird}, there are many attempts to explore the feasibility of Transformers in vision tasks.
DETR~\cite{DETR} and its variants~\cite{DETR_1,DETR_2} effectively remove the need for many hand-designed components like non-maximum suppression procedures and anchor generation by adopting Transformers.
ViT~\cite{ViT} divides an image into $16 \times 16 $ patches and feeds them into a standard Transformer's encoder.
There are also some attempts in semantic segmentation~\cite{Semantic_1,Semantic_2}, lane shape prediction~\cite{lane}, video frame synthesis~\cite{convtransformer}, \etc. To the best of our knowledge, we are the first to introduce Transformers into the online action detection task. In particular, unlike the original auto-regressive Transformer, OadTR adopts a non-auto-regressive Transformer to generate sequences in parallel to improve efficiency.

\begin{figure*}[t!]
\centering{\includegraphics[width=.99\linewidth]{pictures/Networks+decoder.pdf}}
\vspace{-1mm}
\caption{Illustration of the proposed Online Action Detection TRansformer (OadTR).
Given an input streaming video $\mathbf{V}=\left \{ f_{t} \right \}_{t=-T}^{0}$, a task token is attached to the visual features output by the feature extraction network.
Then the token feature sequence is input into the standard Transformer's encoder to model long-range historical temporal dependencies.
Afterward, the decoder of OadTR anticipates the future context information in parallel.
Finally, the predicted future context are involved in classifying the current action. Note that OadTR, including the encoder and decoder, is an end-to-end parallel framework.
}
\label{network_encoder_decoder}
\vspace{-2mm}
\end{figure*}

\section{Methodology}

In this section, we will first introduce the problem definition and then present the proposed OadTR detailedly.

\subsection{Problem description}
Given a video stream that may contain multiple actions, the goal of the task is to identify the actions currently taking place in real-time.
We denote $ \mathbf{V}=\left \{ f_{t} \right \}_{t=-T}^{0} $ as the input streaming video, which needs to classify the current frame chunk $f_{0}$.
We use $y_{0}$ to represent the action category of the current frame chunk $f_{0}$, and $ y_{0}\in \left \{ 0,1,...,C \right \} $, where $ C $ is the total number of the action categories and index $ 0 $  denotes the background category.

\subsection{OadTR}

To explore the potential benefits of the Transformer, we introduce the power of self-attention into the online action detection task.
In this section, we present OadTR, a encoder-decoder conformation.
OadTR adopts an attention mechanism to capture long-range contextual information in the temporal dimension of features.
The schematic diagram of OadTR is shown in Figure~\ref{network_encoder_decoder}.
\subsubsection{Encoder}

\quad Given a streaming video $ V=\left \{ f_{t} \right \}_{t=-T}^{0} $, the feature extractor~\cite{TSN} extracts a 1D feature sequence by collapsing the spatial dimensions.
Then an additional linear projection layer further maps each vectorized frame chunk feature into a $D$-dimensional feature space and $ F=\left \{ token_{t} \right \}_{t=-T}^{0} \in \mathbb{R}^{\left (T+1\right )\times D}$ denotes the resulting token sequence.

\begin{figure}[t!]
\centering
\centering{\includegraphics[width=.99\linewidth]{pictures/analysis-2.pdf}}
\vspace{-1mm}
\caption{\label{figure-analysis-1}Comparison of similarity distribution between the classification features (\ie, features before sending to classifier) and the input features sequence $ \tilde{F} $. Note that \textit{w/o task token}: the output classification features correspond to the token of the $f_{0}$ input; \textit{w/ task token}: the output classification features correspond to the task token.
}
\vspace{-4mm}
\end{figure}
In the encoder, we extend a learnable $token_{class} \in \mathbb{R}^{D} $ to the embedded feature sequence $ F $ and get the combined token feature sequence $ \tilde{F} = Stack( \left \{ token_{t} \right \}_{t=-T}^{0}, token_{class}) \in \mathbb{R}^{\left (T+2\right )\times D} $. Note that $token_{class}$ is used to learn global discriminative features related to the online action detection task.
Intuitively, if there is no $token_{class}$ here, the final feature representation obtained by other tokens will inevitably be biased towards this specified token as a whole, and thus cannot be used to represent this learning task (\ie, \textit{w/o task token} in Figure~\ref{figure-analysis-1}).
In contrast, the semantic embedding of $token_{class}$ can be obtained by adaptively interacting with other tokens in the encoder, which is more suitable for feature representations (\ie, \textit{w/ task token} in Figure~\ref{figure-analysis-1}). We will further confirm the necessity of $token_{class}$ in Sec~\ref{ablation}.

Since there is no frame order information in the encoder, we need to embed position encoding additionally. Position encoding can take two forms: sinusoidal inputs and trainable embeddings. We add position encoding $E_{pos} \in \mathbb{R}^{\left (T+2\right )\times D} $ to the token sequence (\ie, element-wise addition) to retain positional information:
\begin{equation}
X_{0} =\tilde{F}+ E_{pos}
\label{eq1}\\
\end{equation}
In this way, positional information can be kept despite the orderless self-attention.

Multi-head self-attention (MSA) is the core component of the Transformer. Intuitively, the idea behind self-attention is that each token can interact with other tokens and can learn to gather useful semantic information more effectively, which is very suitable for capturing long-range dependencies.
We compute the dot products of the query with all keys and apply a softmax function to obtain the weights on the values.
Generally, the formula for self-attention is defined as:
\begin{align}
{X}'& = {\rm Norm}(X_{0}) \\
{\rm Attention}(Q_{i}; K_{i}; V_{i}) &= {\rm softmax}\left (\frac{Q_{i}K_{i}^{T}}{\sqrt{d_{k}}}\right )V_{i}
\label{eq2}\\
H_{i} &= {\rm Attention}(Q_{i}; K_{i}; V_{i})
\label{eq3}
\end{align}
where $Q_{i}={X}'\mathbf{W}_{i}^{q}$, $K_{i}={X}'\mathbf{W}_{i}^{k}$ and $V_{i}={X}'\mathbf{W}_{i}^{v}$ are linear layers applied on input sequence, and $\mathbf{W}_{i}^{q},\mathbf{W}_{i}^{k},\mathbf{W}_{i}^{v} \in \mathbb{R}^{D \times \frac{D}{N_{head}} }$. Note that queries, keys, and values are all vectors, and ${N_{head}}$ is the number of heads.
$\frac{1}{\sqrt{d_{k}}}$ is a scaling factor, and $d_{k}$ is typically set to $D$. The scaling factor can make the training more stable and accelerate convergence. Subsequently, the outputs of heads $H_{1},H_{2},...,H_{N_{head}}$ are concatenated and feed into a linear layer. The formula is as follows:
\begin{equation}
\hat{H} = Stack\left ( H_{1},H_{2},...,H_{N_{head}} \right )\mathbf{W}_{d} \in \mathbb{R}^{\left (T+2\right ) \times D}
\label{eq4}
\end{equation}
where $\mathbf{W}_{d}$ is a linear projection. Multi-head self-attention allows the encoder to focus on multiple different patterns, which is beneficial to improve the robustness and capacity of the encoder.

Subsequently, it is followed by a two-layered feed-forward network (FFN) with GELU~\cite{GELU} activations. Meanwhile, layernorm~\cite{LN} and residual connections~\cite{resnet} are also applied. The final multiple formulas can be expressed as:
\begin{align}
\hat{H} &={\rm MSA}(Norm(X_{0}))\\
{m'}_{1} &=\hat{H} + X_{0}
\label{eq5}\\
{{m}_{n}} &={\rm FFN}\left ({\rm Norm}({m'}_{n-1})\right ) + {m'}_{n-1}
\\
{m'}_{n} &= {\rm MSA}({\rm Norm}({{m}_{n-1}})) + {{m}_{n-1}}
\label{eq6}
\end{align}
where $ n= 1,2,...,N $, $ N $ is the number of encoding layers and $ {{m}_{N}} \in \mathbb{R}^{\left (T+2\right )\times D} $ denotes the final output feature representations of the encoder.
For the convenience of explanation, we use $ {{m}_{N}^{token}}\in \mathbb{R}^D $ to signify the output representation of the encoder corresponding to the task token.

\subsubsection{Decoder}

\quad When a person is watching a movie, he will not only remember the past, but also make predictions about what will happen in the near future~\cite{bubic2010prediction}.
Consequently, the decoder of OadTR makes use of the observation of past information to predict the actions that will occur in the near future, so as to better learn more discriminative features.

Prediction query $Q_{i} \in \mathbb{R}^{D'}, i=1,2,...,\ell_d$ is also learnable, where $D'$ is the number of query channels. The difference with the original Transformer~\cite{Transformer} is that our decoder decodes the $ \ell_d $ prediction queries in parallel at each decoding layer. The decoder is allowed to utilize semantic information from the encoder through the encoder-decoder cross-attention mechanism.

Here, we use $\widetilde{Q}_{i} \in \mathbb{R}^{D'}, i=1,2,...,\ell_d $ to represent the sequence output after the decoder.

\subsubsection{Training}

\quad In OadTR, we mainly use the encoder to identify the current frame chunk $f_{0}$, and the decoder to predict the coming future. At the same time, the prediction results are used as auxiliary information to better recognize the action.

For the classification task of the current frame chunk, we first concatenate the task-related features in the encoder with the pooled predicted features in the decoder. Then the resulting features go through a full connection layer and a softmax operation for action classification:
\begin{align}
\widetilde{Q} &= Avg\text{-}pool(\widetilde{Q}_{1},\widetilde{Q}_{2},...,\widetilde{Q}_{\ell_d})
\\
p_{0} &= {\rm softmax}(Concat [{m}_{N}^{token},\widetilde{Q} ]\mathbf{W}_{c})
\label{eq10}
\end{align}
where $\mathbf{W}_{c}$ represents full connection layer parameters for classification, and $p_{0} \in \mathbb{R}^{C+1}$.

In addition to the estimated current action, OadTR also outputs predicted features for the next $\ell_d$ time steps. Since future information is available during offline training, in order to ensure that a good feature expression is learned, we also conduct supervised training on future prediction features:
\begin{equation}
\widetilde{p}^{i} = {\rm softmax}(\widetilde{Q}_{i} \mathbf{W}_{c}^{'}), i=1,2,...,\ell_d
\label{eq16}
\end{equation}
Therefore, the final joint training loss is:
\begin{equation}
Loss = {\rm CE}(p_{0},y_{0}) + \lambda \sum_{i=1}^{\ell_d}{\rm CE}(\widetilde{p}_{i}, \widetilde{y}_{i})
\label{eq14}
\end{equation}
where ${\rm CE}$ is the cross entropy loss, $ \widetilde{y}_{i} $ is the actual action category for the next step $i$ and $\lambda$ is a balance coefficient, set to 0.5 in the experiment.
\section{Experiments}
In this section, we evaluate the proposed OadTR on three benchmark datasets: HDD~\cite{HDD}, TVSeries~\cite{online_de}, and THUMOS14~\cite{thumos14} for online action detection. First, we compare the results between our OadTR and start-of-the-art methods. Then, we conduct more detailed ablation studies to evaluate the effectiveness of OadTR.

\begin{table}[t]
    \centering
    \small
\tablestyle{4pt}{1.05}
   \begin{tabular}{L{1.5cm}|C{1.5cm}|C{2.cm}|C{1.5cm}}
Method & Reference &Input & mAP (\%) \\
\shline
        {CNN}\cite{online_de}& ECCV'16 & \multirow{5}{*}{Sensors} & 22.7 \\
        {LSTM}\cite{HDD}&CVPR'18 & & 23.8 \\
        {ED}~\cite{RED}& BMVC'17 & & 27.4 \\
        {TRN}~\cite{TRN}& ICCV'19  & & 29.2 \\

        {\bf{OadTR}} &- & & \bf{29.8} \\

    \end{tabular}
    \vspace{-5pt}
    \caption{Comparison between our OadTR and other state-of-the-art online action detection methods on the HDD~\cite{HDD} dataset in terms of mAP (\%).
    }
    \vspace{-5pt}
    \label{table:HDD_detection}
\end{table}
\begin{table}[t]
\small
\centering
\tablestyle{4pt}{1.05}
\begin{tabular}{L{1.5cm}|C{1.5cm}|C{2.cm}|C{1.5cm}
Method &  Reference & Input & mcAP (\%) \\
\shline
RED\cite{RED} &	BMVC'17 &\multirow{4}{*}{\shortstack{TSN-Anet}}	 & 79.2 \\
TRN\cite{TRN} & ICCV'19	&	 & 83.7 \\
IDN\cite{IDN}  & CVPR'20 &   		 & 84.7 \\
\bf{OadTR}  & -  &  		 & \bf{85.4} \\
\cdashline{1-4}
IDN\cite{IDN}  & CVPR'20 & \multirow{2}{*}{\shortstack{TSN-Kinetics}}  		 & 86.1 \\
\bf{OadTR}    & 	- &	 & \bf{87.2} \\
\end{tabular}
\centering\caption{Comparison between our OadTR and other state-of-the-art online action detection methods on the TVSeries~\cite{online_de} dataset in terms of mcAP (\%). Note that we use the same two-stream features for fair comparison.}
\label{table:TVSeries_detection}
\end{table}
\begin{table}[t]
\small
\centering
\tablestyle{4pt}{1.05}
\begin{tabular}{L{2.2cm}|C{1.4cm}|C{1.8cm}|C{1.1cm}
Method &  Reference & Setting & mAP (\%) \\
\shline
 CNN \cite{vgg}& ICLR'15 & \multirow{5}{*}{Offline} & 34.7 \\
 CNN \cite{two-stream} & NIPS'14 & & 36.2 \\
 LRCN \cite{donahue2015long} & CVPR'15 & & 39.3 \\
 MultiLSTM \cite{yeung2018every} & IJCV'18 & & 41.3 \\
 CDC \cite{CDC} & CVPR'17 & & 44.4 \\ \hline
 RED \cite{RED}& BMVC'17 & \multirow{4}{*}{\begin{tabular}[c]{@{}c@{}}Online \\ (TSN-Anet) \end{tabular}}  & 45.3 \\
 TRN\cite{TRN} & ICCV'19  &	 & 47.2 \\
 IDN\cite{IDN}	& CVPR'20	 &      		 & 50.0 \\
 \bf{OadTR}	& -	 &      		 & \bf{58.3} \\ \cdashline{1-4}
 IDN\cite{IDN} & CVPR'20   &  \multirow{2}{*}{\begin{tabular}[c]{@{}c@{}}Online \\ (TSN-Kinetics) \end{tabular}} 		 & 60.3 \\
 \bf{OadTR}     & -	 & 	 & \bf{65.2} \\
\end{tabular}
\centering\caption{Performance comparison on the THUMOS14~\cite{thumos14} dataset in terms of mAP (\%). OadTR, IDN~\cite{IDN}, TRN~\cite{TRN}, and RED~\cite{RED} use the same two-stream features.}
\label{table:THUMOS14_detection}
\end{table}
\begin{table*}[t]
\small
\centering
\tablestyle{4pt}{1.05}
\begin{tabular}{l|cccccccccc}
& \multicolumn{10}{c}{Portion of action} \\ \cline{2-11}
Method & 0\%-10\% & 10\%-20\% & 20\%-30\% & 30\%-40\% & 40\%-50\% & 50\%-60\% & 60\%-70\% & 70\%-80\% & 80\%-90\% & 90\%-100\% \\
\shline
\multicolumn{1}{l|}{CNN \cite{online_de}} & 61.0 & 61.0 & 61.2 & 61.1 & 61.2 & 61.2 & 61.3 & 61.5 & 61.4 & 61.5 \\
\multicolumn{1}{l|}{LSTM \cite{online_de}} & 63.3 & 64.5 & 64.5 & 64.3 & 65.0 & 64.7 & 64.4 & 64.4 & 64.4 & 64.3 \\
\multicolumn{1}{l|}{FV-SVM \cite{online_de}} & 67.0 & 68.4 & 69.9 & 71.3 & 73.0 & 74.0 & 75.0 & 75.4 & 76.5 & 76.8 \\
\multicolumn{1}{l|}{TRN \cite{TRN}} & 78.8 & 79.6 & 80.4 & 81.0 & 81.6 & 81.9 & 82.3 & 82.7 & 82.9 & 83.3 \\
\multicolumn{1}{l|}{IDN\cite{IDN}}  & \bf{80.6} & 81.1 & 81.9 & 82.3 & 82.6 & 82.8 & 82.6 & 82.9 & 83.0 & 83.9 \\
\multicolumn{1}{l|}{\bf{OadTR}}  & 79.5 & \bf{83.9} & \bf{86.4} & \bf{85.4} & \bf{86.4} & \bf{87.9} & \bf{87.3} & \bf{87.3} & \bf{85.9} & \bf{84.6} \\
\cdashline{1-11}
\multicolumn{1}{l|}{IDN (Kinetics)\cite{IDN}}  & \bf{81.7} & 81.9 & 83.1 & 82.9 & 83.2 & 83.2 & 83.2 & 83.0 & 83.3 & 86.6 \\
\multicolumn{1}{l|}{ \bf{OadTR} {(Kinetics)}}  & 81.2 & \bf{84.9} & \bf{87.4} & \bf{87.7} & \bf{88.2} & \bf{89.9} & \bf{88.9} & \bf{88.8} & \bf{87.6} & \bf{86.7} \\
\end{tabular}
\caption{Performance comparison for different portions of actions on the TVSeries dataset in terms of mcAP (\%). Note that the corresponding portions of actions are only used to compute mcAP after detecting current actions on all frame chunks in an online manner.}
\label{tab6}
\end{table*}
\subsection{Dataset and setup}
\noindent \textbf{HDD.} This dataset includes approximately 104 hours of driving actions collected in the San Francisco Bay Area, and there are a total of 11 action categories. The dataset consists of 137 sections and provides various non-visual sensors collected by the vehicle's controller area network bus. We use 100 sections for training and 37 sections for evaluation.

\noindent \textbf{TVSeries.} TVSeries contains six popular TV series, about 150 minutes for each, about 16 hours in total. The dataset totally includes 30 actions, and every action occurs at least 50 times in the dataset. TVSeries contains many unconstrained perspectives and a wide variety of backgrounds.

\noindent \textbf{THUMOS14.} This dataset has 1010 validation videos and 1574 testing videos with 20 classes. For the online action detection task, there are 200 validation videos and 213 testing videos labeled with temporal annotations. As in the previous works~\cite{TRN,IDN}, we train our model on the validation set and evaluate on the test set.

\vspace{+3pt}
\noindent \textbf{Implementation details.} For feature extractor, following previous works~\cite{RED,TRN,IDN}, we adopt the two-stream network~\cite{TSN} (3072-dimensions) pre-trained on ActivityNet v1.3~\cite{activitynet} (TSN-Anet), where spatial and temporal sub-networks adopt ResNet-200~\cite{resnet} and BN-Inception~\cite{BN} separately. For fair quantitative comparison with~\cite{IDN}, we also conduct experiments with the same TSN features (4096-dimensions) pre-trained on Kinetics~\cite{I3D} (TSN-Kinetics).

In terms of training, we implement our proposed OadTR in PyTorch and conduct all experiments with Nvidia V100 graphics cards. Without those bells and whistles, we use Adam~\cite{adam} for optimization, the batch size is set to 128, the learning rate is set to 0.0001, and weight decay is 0.0005. Unless otherwise specified, we set $T$ to 31 for HDD dataset and 63 for both TVSeries and THUMOS14 dataset.

\vspace{+3pt}
\noindent \textbf{Evaluation metric.} To evaluate the performance of OadTR, following the previous methods~\cite{online_de,RED,TRN,IDN}, we report per-frame mean Average Precision (mAP) on HDD and THUMOS14 dataset, and per-frame mean calibrated Average Precision~\cite{online_de} (mcAP) on TVSeries. The mAP is widely used, which needs to average the average precision (AP) of each action class. The calibrated Average Precision (cAP) can be formulated as:
\begin{align}
cPrec &= \frac{TP}{TP+\frac{FP}{w}}
\\
cAP &= \frac{\sum_{k}cPrec(k) \times {I}(k) }{\sum TP}
\end{align}
where ${I}(k)$ is equal to 1 if frame $ K $ is a TP. The coefficient $w$ is the ratio between negative and positive frames.

\begin{table*}[t!]\centering
\hspace{1mm}
\subfloat[Ablation study of the effectiveness of our proposed components on the HDD dataset.  \label{tab:HDD_compare_1}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}

\footnotesize
\# Method & \small mAP (\%) \\
\shline
 LSTM~\cite{HDD} & 23.8 \\
 Encoder-only (Baseline) & 28.7 \\
 \hline
 Baseline + TT & 29.0 \\
 Baseline + DE & 29.2 \\
 Baseline + TT + DE (OadTR) &  \bf{29.8}\\

\end{tabular}}
\hspace{4mm}
\subfloat[Ablation study of the effectiveness of our proposed components on the TVSeries dataset. \label{tab:TVSeries_compare_2}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}

\footnotesize
\# Method & \small mcAP (\%) \\
\shline
 LSTM~\cite{IDN} & 80.9 \\
 Encoder-only (Baseline) & 84.8 \\
 \hline
 Baseline + TT & 85.0 \\
 Baseline + DE & 85.1 \\
 Baseline + TT + DE (OadTR) &  \bf{85.4}\\
\end{tabular}}
\hspace{4mm}
\subfloat[Ablation study of the effectiveness of our proposed components on the THUMOS14 dataset. \label{tab:THUMOS14_compare_3}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}

\footnotesize
\# Method & \small mAP (\%) \\
\shline
 LSTM~\cite{IDN} & 46.3 \\
 Encoder-only (Baseline) & 55.8 \\
 \hline
 Baseline + TT & 56.9 \\
 Baseline + DE & 56.7 \\
 Baseline + TT + DE (OadTR) &  \bf{58.3}\\
\end{tabular}}
\hspace{1mm}

\vspace{2mm}
\subfloat[Ablation study of different position encoding manners on the HDD dataset.  \label{tab:HDD_compare_1}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}

\footnotesize
\# Position encoding & \small mAP (\%) \\
\shline
 No Position & 28.8 \\
 Fixed Position & 29.3 \\
 Learned Position &  \bf{29.8}\\

\multicolumn{2}{c}{~}\\
\multicolumn{2}{c}{~}\\
\multicolumn{2}{c}{~}\\
\end{tabular}}
\hspace{1mm}
\subfloat[Ablation study of the head number on the THUMOS14 dataset. \label{tab:THUMOS14_compare_2}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}
 \footnotesize
\# Head & \small mAP (\%) \\
\shline
 Head = 1 & 57.4 \\
 Head = 2 & 57.7 \\
 Head = 4 &  \bf{58.3}\\

 Head = 8  & 57.7 \\
 Head = 16  & 57.8 \\
Head = 32  & 57.4 \\
\end{tabular}}
\hspace{1mm}
\subfloat[Ablation study of the query dimensions on the THUMOS14 dataset. \label{tab:THUMOS14_compare_3}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}
 \footnotesize
\# Dim & \small mAP (\%) \\
\shline
 Dim = 128 & 56.4 \\
 Dim = 256 & 57.1 \\
 Dim = 512 & 57.3 \\
 Dim = 1024 & \bf{58.3} \\
 Dim = 1536 & 57.7 \\
 Dim = 2048 & 57.6 \\
\end{tabular}}
\hspace{1mm}
\subfloat[Generalization evalation. The results indicate the generalization and superiority of our model design. \label{tab:sparse_compare_3}]{
\tablestyle{4pt}{1.05}
\footnotesize
\begin{tabular}{l|c}
 \footnotesize
\# Generalization & \small m(c)AP (\%) \\
\shline
 Vanilla Transformer (HDD) & 29.8\\
 Sparse Transformer (HDD) & 29.6 \\
 Vanilla Transformer (TVSeries) & 85.4 \\
 Sparse Transformer (TVSeries) & 85.0 \\
 Vanilla Transformer (THUMOS14) & 58.3 \\
 Sparse Transformer (THUMOS14) & 58.1 \\
\end{tabular}}
\\
\caption{Ablation studies.
\label{table:Ablation_study}}
\vspace{-4mm}
\end{table*
\subsection{Compared with state-of-the-art methods}
To evaluate the performance, we compare our proposed OadTR and other state-of-the-art methods~\cite{RED,TRN,IDN} on HDD, TVSeries, and THUMOS14 datasets.
Noted that we use the same network parameter settings (\eg, $N=3$, $M=5$) when comparing different datasets.
As illustrated in Table~\ref{table:HDD_detection}, our OadTR achieves state-of-the-art performance and improves mAP from 29.2$\%$ to 29.8$\%$ on the HDD dataset, demonstrating that our OadTR can achieve an overall performance promotion of online action detection. It could be attributed to that our OadTR introduces Transformers to obtain global historical information and the future context efficiently.

Table~\ref{table:TVSeries_detection} compares recent online action detection methods on the TVSeries dataset. To ensure a fair comparison, we adopt the same video features. The results signify that our OadTR can achieve good performance in the case of different video feature inputs. The reason for the better results of TSN-Kinetics may be that the categories of Kinetics are more diverse and contains much common generalizable presentations.

As shown in Table~\ref{table:THUMOS14_detection}, we also conduct a comprehensive comparison on the THUMOS14 dataset. Specifically, performance is improved by 8.3$\%$ (50.0$\%$ $vs.$ 58.3$\%$ ) under TSN-Anet feature input and 4.9$\%$ (60.3$\%$ $vs.$ 65.2$\%$ ) under TSN-Kinetics feature input. The above results indicate the effectiveness of our proposed OadTR.

When only a fraction of each action is considered, we compared OadTR with previous methods. Table~\ref{tab6} shows that our OadTR significantly outperforms state-of-the-art methods~\cite{TRN,IDN} at most time stages. Specifically, this indicates the superiority of OadTR in recognizing actions at early stages as well as all stages. It could be attributed to the ability of our OadTR to efficiently model temporal dependencies.

\subsection{Ablation studies}
\label{ablation}
To facilitate our analysis of the model, we take the encoder without task token as our baseline.
We further conduct detailed ablation studies to evaluate different components of the proposed framework, include the following:\\
\textbf{\textit{Encoder-only (Baseline)}:} We adopt the encoder in the original Transformer~\cite{Transformer} and apply it directly to the online action detection task. Note that compared with our OadTR's encoder, the task token is missing, and the classifier is applied to the last output representation (\ie, corresponding to $f_{0}$) of the Transformer's encoder.
\\
\textbf{\textit{Baseline + TT}:} We incorporate \textit{Baseline} and the task token (TT) together, which is our OadTR's encoder. This method adds a task-related token, and we use ablation experiments to illustrate the necessity of this token. \\
\textbf{\textit{Baseline + DE}:} In this method, we add the decoder (DE) of the prediction task in OadTR to the baseline method to test and verify the function of the decoder. \\
\textbf{\textit{Baseline + TT + DE (OadTR)}:} This is the method we propose in this paper, adding task token and decoder to the baseline method together.

In Table~\ref{table:Ablation_study} (a-c), we report the performance comparison experiments of the above methods on HDD, TVSeries, and THUMOS14 datasets. The results of \textit{Baseline + TT} demonstrate that using the additional task token is helpful for action classification. The results of \textit{Baseline + DE} explain the remarkable power of the auxiliary prediction task. When combined with the above two improvements, our OadTR (\ie, \textit{Baseline + TT + DE}) achieves the best results on three datasets. Specifically, when compared with the baseline method, our OadTR approach has improved by 1.1$\%$, 0.6$\%$, and 2.5$\%$ on HDD, TVSeries, and THUMOS14 datasets, respectively.
\vspace{+3pt}

\begin{table}[t]
    \centering
    \footnotesize
\tablestyle{4pt}{1.05}
    \begin{tabular}
        {@{\;\;}l@{\;\;}@{\;\;}|l@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}}

        & & \multicolumn{4}{c}{Decoder steps ($\ell_d$)}\\

\cline{3-6}
        Dataset & Task & 2 & 4 & 8 & 16 \\

\shline
        \multirow{2}{*}{HDD}
        & {Online Action Detection}   & 28.3 & 28.4 & \bf{29.8} & 28.8 \\
        & {Action Anticipation}       & 27.2 & 26.2 & 23.0 & 16.9 \\

\hline
        \multirow{2}{*}{TVSeries}
        & {Online Action Detection}   & 85.3 & 85.2 & \bf{85.4} & 85.2 \\
        & {Action Anticipation}       & 81.8 & 80.4 & 77.8 & 74.3 \\

\hline
        \multirow{2}{*}{THUMOS14}
        & {Online Action Detection}   & 57.4 & 57.8 & \bf{58.3} & 58.0 \\
        & {Action Anticipation}       & 53.5 & 51.0 & 45.9 & 40.7 \\

    \end{tabular}
    \vspace{-5pt}
    \caption{
        Online action detection and action anticipation results of our proposed OadTR
        with decoder steps $\ell_d=2,4,8,16$.
    }
    \vspace{-8pt}
    \label{table:decoder}
\end{table}
\begin{table}[t]
    \centering
    \footnotesize
    \begin{subtable}[t]{\linewidth}
\centering
\tablestyle{4pt}{1.05}
    \begin{tabular}
        {@{\;\;}l@{\;\;}@{\;\;}|l@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c}

        & & \multicolumn{6}{c}{Encoding layers ($N,M=5$)}\\

\cline{3-8}
        Method & Dataset & 1 & 2 & 3 & 4 & 5 & 6 \\

\shline
        \multirow{3}{*}{OadTR}
        & {HDD}   & 28.8 & 29.0 & \bf{29.8} & 29.5 & 28.4 & 28.4 \\
        & {TVSeries}   & 85.4 & \bf{85.5} & 85.4 & 85.3 & 84.8 & 85.0 \\
        & {THUMOS14}   & 57.5 & 56.9 & \bf{58.3} & 57.4 & 56.9 & 56.6 \\

    \end{tabular}
    \vspace{-3pt}
    \caption{
        Online action detection results of different encoding layers. Note that we fixed $M=5$ for simplicity.
    }
    \vspace{+3pt}
\end{subtable}
\begin{subtable}[t]{\linewidth}
        \centering
\tablestyle{4pt}{1.05}
\begin{tabular}
        {@{\;\;}l@{\;\;}@{\;\;}|l@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c}

        & & \multicolumn{6}{c}{Decoding layers ($M, N=3$)}\\

\cline{3-8}
        Method & Dataset & 1 & 2 & 3 & 4 & 5 & 6 \\

\shline
        \multirow{3}{*}{OadTR}
        & {HDD}   & 27.7 & 28.0 & 29.5 & 29.5 & \bf{29.8} & 28.6 \\
        & {TVSeries}   & 84.8 & 84.8 & 85.2 & 85.4 & 85.4 & \bf{85.5} \\
        & {THUMOS14}   & 57.4 & 57.9 & 56.9 & 57.2 & \bf{58.3} & 56.6 \\

    \end{tabular}
    \vspace{-3pt}
    \caption{
        Online action detection results of different decoding layers. Note that we fixed $N=3$ for simplicity.
    }
    \vspace{-5pt}
\end{subtable}
\caption{
        Ablation study of encoding layers $N$ and decoding layers $M$ using TSN-Anet features.
    }
    \label{table:encoder_decoder}
\vspace{-8pt}
\end{table}
\begin{table*}[t!]
    \small
    \centering
\tablestyle{4pt}{1.05}
    \begin{subtable}[t]{\textwidth}
        \centering
        \begin{tabular}
            {@{\;}@{\;}l@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}|c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\,}@{\;}r@{\;\;\;}@{\;}@{\;}}

            & \multicolumn{8}{c}{Time predicted into the future (seconds)} & \\

\cline{2-9}
Method & 0.25s & 0.5s & 0.75s & 1.0s & 1.25s & 1.5s & 1.75s & 2.0s &\;\;\;\; \;\;\;\;Avg \\

\shline
            {ED~\cite{RED}} & 78.5 & 78.0 & 76.3 & 74.6 & 73.7 & 72.7 & 71.7 & 71.0 & 74.5 \\
            {RED~\cite{RED}} & 79.2 & 78.7 & 77.1 & 75.5 & 74.2 & 73.0 & 72.0 & 71.2 & 75.1 \\
            {TRN~\cite{TRN}} & 79.9 & 78.4 & 77.1 & 75.9 & 74.9 & 73.9 & 73.0 & 72.3 & 75.7 \\
            {\bf{OadTR}} & 81.9 & 80.6 & 79.4 & 78.2 & 77.1 & 76.0 & 75.2 & 74.3 & \textbf{77.8} \\ \cdashline{1-10}
            {\bf{OadTR (Kinetics)}}& 84.1 & 82.6 & 81.3 & 80.1 & 78.9 & 77.7 & 76.7 & 75.7 & \textbf{79.1} \\

        \end{tabular}
        \vspace{-3pt}
        \caption{
            Results on the TVSeries dataset in terms of mcAP (\%).
        }
        \vspace{3pt}
    \end{subtable}
\tablestyle{4pt}{1.05}
    \begin{subtable}[t]{\textwidth}
        \centering
        \begin{tabular}
            {@{\;}@{\;}l@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}|c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}@{\;}c@{\;}@{\,}@{\;}r@{\;\;\;}@{\;}@{\;}}

            & \multicolumn{8}{c}{Time predicted into the future (seconds)} & \\

\cline{2-9}
Method & 0.25s & 0.5s & 0.75s & 1.0s & 1.25s & 1.5s & 1.75s & 2.0s &\;\;\;\; \;\;\;\;Avg \\

\shline
            {ED~\cite{RED}} & 43.8 & 40.9 & 38.7 & 36.8 & 34.6 & 33.9 & 32.5 & 31.6 & 36.6 \\
            {RED~\cite{RED}} & 45.3 & 42.1 & 39.6 & 37.5 & 35.8 & 34.4 & 33.2 & 32.1 & 37.5 \\
            {TRN~\cite{TRN}} & 45.1 & 42.4 & 40.7 & 39.1 & 37.7 & 36.4 & 35.3 & 34.3 & 38.9 \\
            {\bf{OadTR}} & 50.2 & 49.3 & 48.1 & 46.8 & 45.3 & 43.9 & 42.4 & 41.1 & \textbf{45.9} \\ \cdashline{1-10}
            {\bf{OadTR (Kinetics)}} & 59.8 & 58.5 & 56.6 & 54.6 & 52.6 & 50.5 & 48.6 & 46.8 & \textbf{53.5} \\

        \end{tabular}
        \vspace{-3pt}
        \caption{
            Results on the THUMOS14 dataset in terms of mAP (\%).
        }
        \vspace{3pt}
    \end{subtable}
    \vspace{-10pt}
    \caption{
        Action anticipation results of our OadTR compared to state-of-the-art
        methods using the same two-stream features.
    }
    \vspace{-2pt}
    \label{table:anticipation}
\end{table*}
\begin{table}[t]
    \centering
    \footnotesize
\tablestyle{4pt}{1.05}
    \begin{tabular}
        {@{\;\;}l@{\;\;}@{\;\;}|l@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}@{\;\;}c@{\;\;}}

        & & \multicolumn{3}{c}{Settings}\\

\cline{3-5}
        Method & Dataset & Max-pool & Avg-pool & w/o encoder  \\

\shline
        \multirow{3}{*}{OadTR}
        & {HDD}   & 29.2 & \bf{29.8} & 26.1 \\

       %
        & {TVSeries}   & 85.1 & \bf{85.4} & 80.8 \\

        %
        & {THUMOS14}   & 57.9 & \bf{58.3} & 53.5

    \end{tabular}
    \vspace{-5pt}
    \caption{
        Comparison of different fusion methods and the necessity of the encoder feature encoding.
    }
    \vspace{-3pt}
    \label{table:aggregation_type}
\end{table}
\noindent\textbf{Importance of position encoding.} To demonstrate the importance of using position encoding, we organized some comparative experiments. As shown in Table~\ref{table:Ablation_study} (d), position encoding is necessary, and the learnable position encoding achieves the best result.
\vspace{+3pt}
\\
\textbf{Effect of head number.} Multi-head self-attention is a critical component. Here we study the impact of different head numbers of the decoder on performance. We can find that a optimal result is achieved when the number of heads is 4 (Table~\ref{table:Ablation_study} (e)).
\vspace{+3pt}
\\
\textbf{Effect of query dimensions.} We conduct experiments to examine how different query dimensions affect online action detection performance. As shown in Tabel~\ref{table:Ablation_study} (f), when the number of feature dimensions is relatively small (\eg, 128), the model capacity is limited and the performance is relatively poor. As the number of feature dimensions gradually increases, the model capacity increases, and performance improves. However, when it exceeds a specific value (\eg, 1024), over-fitting may occur.
\vspace{+3pt}
\\
\textbf{Generalizability.} To further investigate the generalization of our OadTR for many Transformer variants, we replace the standard Transformer~\cite{Transformer} with Sparse Transformer~\cite{sparse_Transformer}. As illustrated in Table~\ref{table:Ablation_study} (g), we can find that the performance is still good after replacement. Generally, Sparse Transformer can reduce computational consumption but leads to a little performance degradation.
\vspace{+3pt}
\\
\textbf{Effect of decoder step count.} The step size used to predict the future also has an impact on performance. In Table~\ref{table:decoder}, we compare four step sizes (\ie, 2, 4, 8, and 16), and the results denote that $steps=8$ achieves the best results on the three datasets.
\vspace{+3pt}
\\
\textbf{Effect of encoding layers $N$ and decoding layers $M$.} To further study the impact of different encoding and decoding layers on performance, additional experiments are conducted, and results are shown in Table~\ref{table:encoder_decoder}. In most cases, the best results are achieved when $N=3, M=5$. However, there will also be fluctuations, such as on the TVSeries dataset.
\vspace{+3pt}
\\
\textbf{Feature aggregation type.} We also conduct experiments to explore different types that aggregate future and current features. We can notice that Avg-pool is better than Max-pool (Table~\ref{table:aggregation_type}). The reason may be that the predicted deep semantic representations of different time steps all have a specific promotion effect on the current classification. Simultaneously, the results of \textit{w/o encoder} also indicate the necessity of the encoder to learn discriminative features.

\begin{figure}[t!]
\centering
{\centering{\includegraphics[width=.48\linewidth]{pictures/thumos_idu-select-0.pdf}}}
\hspace{1mm}
\centering{\includegraphics[width=.48\linewidth]{pictures/thumos_OadTR-select-0.pdf}}
\vspace{-6mm}
\caption{\label{figure-visual-1}The t-SNE visualization of the classification embedding logits. Different colors correspond to different action categories from the THUMOS14 dataset. The mutual color correspondences include: \textcolor[RGB]{241,125,31}{CliffDiving}, \textcolor[RGB]{148,181,81}{HighJump}, \textcolor[RGB]{104,50,6}{PoleVault} and \textcolor[RGB]{211,222,252}{Shotput}. Better view in colored PDF.
}
\vspace{-4mm}
\end{figure}
\begin{figure}[t!]
\centering
\centering{\includegraphics[width=.99\linewidth]{pictures/visual.pdf}}
\vspace{-1mm}
\caption{\label{figure-visual-2}Attention visualization maps. They indicate how much attention is paid to parts of the input streaming video.
}
\vspace{-4mm}
\end{figure}
\subsection{Action anticipation}
In our proposed OadTR, we introduce predicted future information to identify current actions. To evince the accuracy of our predictions, we also conduct experiments to compare with other methods. Table~\ref{table:anticipation} demonstrates that OadTR outperforms the current state-of-the-art method~\cite{TRN} by a large margin.
In particular, the performance of OadTR is 2.1$\%$ higher than TRN~\cite{TRN} on the TVSeries dataset and 7.0$\%$ higher on THUMOS14. Furthermore, the performance of OadTR can also be further improved when pre-training on Kinetics.
\subsection{Qualitative evaluation}
For better analysis, we visualize the classification results in Figure~\ref{figure-visual-1}. Obviously, by visualizing all the test samples of the four action categories, we can observe that our OadTR has better separability compared with the current state-of-the-art IDN~\cite{IDN}.
Further, we show OadTR's multi-head attention visualization results in Figure~\ref{figure-visual-2}, which indicates the importance of the multi-head design to learn more complex and comprehensive relations between different neighboring frame chunks.

\section{Conclusion}
In this paper, we have proposed a new online action detection framework built upon Transformers, termed OadTR.
In contrast to existing RNN based methods that process the sequence one by one recursively and hard to be optimized, we aim to design a direct end-to-end parallel network.
OadTR can recognize current actions by encoding historical information and predicting future context simultaneously.
Extensive experiments are conducted and verify the effectiveness of OadTR. Particularly, OadTR achieves higher training and inference speeds than current RNN based approaches, and obtains significantly better performance compared to the state-of-the-art methods.
In the future, we will extend our OadTR model to more tasks such as action recognition, spatio-temporal action detection, \etc.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


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