Online Generic Event Boundary Detection

• URL: http://arxiv.org/abs/2510.06855v1
• Date: Wed, 08 Oct 2025 10:23:45 GMT
• Title: Online Generic Event Boundary Detection
• Authors: Hyungrok Jung, Daneul Kim, Seunggyun Lim, Jeany Son, Jonghyun Choi,
• Abstract summary: We introduce a new task, Online Generic Event Boundary Detection (On-GEBD), aiming to detect boundaries of generic events immediately in streaming videos.<n>This task faces unique challenges of identifying subtle, taxonomy-free event changes in real-time, without the access to future frames.<n>We propose a novel On-GEBD framework, inspired by Event Theory (EST) which explains how humans segment ongoing activity into events by leveraging discrepancies between predicted and actual information.
• Score: 27.34486732049466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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