Crowd Event Recognition using HOG Tracker

Carolina Garate 1 Piotr Bilinski 1 François Bremond 1
1 PULSAR - Perception Understanding Learning Systems for Activity Recognition
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The recognition in real time of crowd dynamics in public places are becoming essential to avoid crowd related disasters and ensure safety of people. We present in this paper a new approach for Crowd Event Recognition. Our study begins with a novel tracking method, based on HOG descriptors, to finally use pre-defined models (i.e. crowd scenarios) to recognize crowd events. We define these scenarios using statistics analysis from the data sets used in the experimentation. The approach is characterized by combining a local analysis with a global analysis for crowd behavior recognition. The local analysis is enabled by a robust tracking method, and global analysis is done by a scenario modeling stage.
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Carolina Garate, Piotr Bilinski, François Bremond. Crowd Event Recognition using HOG Tracker. Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), Dec 2009, Snowbird, UT, United States. pp.1-6, ⟨10.1109/PETS-WINTER.2009.5399727⟩. ⟨inria-00515197v2⟩

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