7 May 2012 Tracking individuals in surveillance video of a high-density crowd
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Abstract
Video cameras are widely used for monitoring public areas, such as train stations, airports and shopping centers. When crowds are dense, automatically tracking individuals becomes a challenging task. We propose a new tracker which employs a particle filter tracking framework, where the state transition model is estimated by an optical-flow algorithm. In this way, the state transition model directly uses the motion dynamics across the scene, which is better than the traditional way of a pre-defined dynamic model. Our result shows that the proposed tracker performs better on different tracking challenges compared with the state-of-the-art trackers, while also improving on the quality of the result.
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Ninghang Hu, Ninghang Hu, Henri Bouma, Henri Bouma, Marcel Worring, Marcel Worring, } "Tracking individuals in surveillance video of a high-density crowd", Proc. SPIE 8399, Visual Information Processing XXI, 839909 (7 May 2012); doi: 10.1117/12.918604; https://doi.org/10.1117/12.918604
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