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A generic framework for video understanding applied to group behavior recognition

Sofia Zaidenberg 1 Bernard Boulay 1 François Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.
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https://hal.inria.fr/hal-00702179
Contributor : Sofia Zaidenberg <>
Submitted on : Wednesday, June 20, 2012 - 2:41:30 PM
Last modification on : Friday, July 3, 2020 - 10:00:19 AM
Long-term archiving on: : Thursday, December 15, 2016 - 9:31:48 AM

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Sofia Zaidenberg, Bernard Boulay, François Bremond. A generic framework for video understanding applied to group behavior recognition. AVSS 2012 - 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, IEEE Computer Society, Sep 2012, Beijing, China. pp.136 -142, ⟨10.1109/AVSS.2012.1⟩. ⟨hal-00702179⟩

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