Follow Anything: Open-set detection, tracking, and following in
  real-time
Tracking and following objects of interest is critical to several robotics
use cases, ranging from industrial automation to logistics and warehousing, to
healthcare and security. In this paper, we present a robotic system to detect,
track, and follow any object in real-time. Our approach, dubbed ``follow
anything'' (FAn), is an open-vocabulary and multimodal model -- it is not
restricted to concepts seen at training time and can be applied to novel
classes at inference time using text, images, or click queries. Leveraging rich
visual descriptors from large-scale pre-trained models (foundation models), FAn
can detect and segment objects by matching multimodal queries (text, images,
clicks) against an input image sequence. These detected and segmented objects
are tracked across image frames, all while accounting for occlusion and object
re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial
vehicle) and report its ability to seamlessly follow the objects of interest in
a real-time control loop. FAn can be deployed on a laptop with a lightweight
(6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To
enable rapid adoption, deployment, and extensibility, we open-source all our
code on our project webpage at https://github.com/alaamaalouf/FollowAnything .
We also encourage the reader the watch our 5-minutes explainer video in this
https://www.youtube.com/watch?v=6Mgt3EPytrw .
64d5af0595cf13a3818163dd
Alaa Maalouf
claimed_verified
2023-08-13T00:11:26.704Z
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/avatars/f45d58fb1428acd06af19fd701407cef.svg
Alaa Maalouf
user
alaamaalouf
64d5af0595cf13a3818163de
Ninad Jadhav
64d5af0595cf13a3818163df
Krishna Murthy Jatavallabhula
64d5af0595cf13a3818163e0
Makram Chahine
64d5af0595cf13a3818163e1
Daniel M. Vogt
64d5af0595cf13a3818163e2
Robert J. Wood
64d5af0595cf13a3818163e3
Antonio Torralba
64d5af0595cf13a3818163e4
Daniela Rus
Follow Anything: Open-set detection, tracking, and following in
  real-time
Tracking and following objects of interest is critical to several robotics
use cases, ranging from industrial automation to logistics and warehousing, to
healthcare and security. In this paper, we present a robotic system to detect,
track, and follow any object in real-time. Our approach, dubbed ``follow
anything'' (FAn), is an open-vocabulary and multimodal model -- it is not
restricted to concepts seen at training time and can be applied to novel
classes at inference time using text, images, or click queries. Leveraging rich
visual descriptors from large-scale pre-trained models (foundation models), FAn
can detect and segment objects by matching multimodal queries (text, images,
clicks) against an input image sequence. These detected and segmented objects
are tracked across image frames, all while accounting for occlusion and object
re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial
vehicle) and report its ability to seamlessly follow the objects of interest in
a real-time control loop. FAn can be deployed on a laptop with a lightweight
(6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To
enable rapid adoption, deployment, and extensibility, we open-source all our
code on our project webpage at https://github.com/alaamaalouf/FollowAnything .
We also encourage the reader the watch our 5-minutes explainer video in this
https://www.youtube.com/watch?v=6Mgt3EPytrw .
FAn, an open-vocabulary and multimodal robotic system, detects, tracks, and follows objects in real-time using pre-trained models and multimodal queries, and is deployable on a laptop with a lightweight GPU.
follow anything open-vocabulary multimodal model pre-trained models visual descriptors multimodal queries object detection object segmentation object tracking occlusion object re-emergence micro aerial vehicle real-time control loop