Towards Unsupervised Sudden Group Movement Discovery for Video Surveillance

Sofia Zaidenberg 1 Piotr Bilinski 1 Francois Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper presents a novel and unsupervised approach for discovering "sudden" movements in video surveillance videos. The proposed approach automatically detects quick motions in a video, corresponding to any action. A set of possible actions is not required and the proposed method successfully detects potentially alarm-raising actions without training or camera calibration. Moreover, the system uses a group detection and event recognition framework to relate detected sudden movements and groups of people, and provide a semantical interpretation of the scene. We have tested our approach on a dataset of nearly 8 hours of videos recorded from two cameras in the Parisian subway for a European Project. For evaluation, we annotated 1 hour of sequences containing 50 sudden movements.
Keywords : Event detection Motion estimation Anomaly estimation Situation awareness Scene Understanding Group Activity Recognition Stream Selection
Type de document :
Communication dans un congrès
Sebastiano Battiato. VISAPP - 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - 2014, Jan 2014, Lisbon, Portugal. SCITEPRESS Digital Library, 2014
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Sofia Zaidenberg, Piotr Bilinski, Francois Bremond. Towards Unsupervised Sudden Group Movement Discovery for Video Surveillance. Sebastiano Battiato. VISAPP - 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - 2014, Jan 2014, Lisbon, Portugal. SCITEPRESS Digital Library, 2014. 〈hal-00878580〉

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