Online Generic Event Boundary Detection
Generic Event Boundary Detection (GEBD) aims to interpret long-form videos
through the lens of human perception. However, current GEBD methods require
processing complete video frames to make predictions, unlike humans processing
data online and in real-time. To bridge this gap, we introduce a new task,
Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries
of generic events immediately in streaming videos. This task faces unique
challenges of identifying subtle, taxonomy-free event changes in real-time,
without the access to future frames. To tackle these challenges, we propose a
novel On-GEBD framework, Estimator, inspired by Event Segmentation Theory (EST)
which explains how humans segment ongoing activity into events by leveraging
the discrepancies between predicted and actual information. Our framework
consists of two key components: the Consistent Event Anticipator (CEA), and the
Online Boundary Discriminator (OBD). Specifically, the CEA generates a
prediction of the future frame reflecting current event dynamics based solely
on prior frames. Then, the OBD measures the prediction error and adaptively
adjusts the threshold using statistical tests on past errors to capture
diverse, subtle event transitions. Experimental results demonstrate that
Estimator outperforms all baselines adapted from recent online video
understanding models and achieves performance comparable to prior offline-GEBD
methods on the Kinetics-GEBD and TAPOS datasets.
68e752627ae125f9582e6ac1
Hyungrok Jung
68e752627ae125f9582e6ac2
Daneul Kim
claimed_verified
2025-10-09T06:49:46.233Z
636b20591340f879a2eb98d0
/avatars/4fc5cb13f916bcbc842ccf387bd5f6c0.svg
Daneul Kim
user
carpedkm
68e752627ae125f9582e6ac3
Seunggyun Lim
68e752627ae125f9582e6ac4
Jeany Son
68e752627ae125f9582e6ac5
Jonghyun Choi
Online Generic Event Boundary Detection
Generic Event Boundary Detection (GEBD) aims to interpret long-form videos
through the lens of human perception. However, current GEBD methods require
processing complete video frames to make predictions, unlike humans processing
data online and in real-time. To bridge this gap, we introduce a new task,
Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries
of generic events immediately in streaming videos. This task faces unique
challenges of identifying subtle, taxonomy-free event changes in real-time,
without the access to future frames. To tackle these challenges, we propose a
novel On-GEBD framework, Estimator, inspired by Event Segmentation Theory (EST)
which explains how humans segment ongoing activity into events by leveraging
the discrepancies between predicted and actual information. Our framework
consists of two key components: the Consistent Event Anticipator (CEA), and the
Online Boundary Discriminator (OBD). Specifically, the CEA generates a
prediction of the future frame reflecting current event dynamics based solely
on prior frames. Then, the OBD measures the prediction error and adaptively
adjusts the threshold using statistical tests on past errors to capture
diverse, subtle event transitions. Experimental results demonstrate that
Estimator outperforms all baselines adapted from recent online video
understanding models and achieves performance comparable to prior offline-GEBD
methods on the Kinetics-GEBD and TAPOS datasets.
A novel framework for real-time event boundary detection in streaming videos uses prediction and error measurement to identify subtle event changes.
Online Generic Event Boundary Detection On-GEBD Event Segmentation Theory Consistent Event Anticipator Online Boundary Discriminator prediction error statistical tests Kinetics-GEBD TAPOS datasets