Tracking individuals in surveillance video of a high-density crowd

Ninghang Hu; Henri Bouma; Marcel Worring

doi:10.1117/12.918604

7 May 2012 Tracking individuals in surveillance video of a high-density crowd

Ninghang Hu, Henri Bouma, Marcel Worring

Proceedings Volume 8399, Visual Information Processing XXI; 839909 (2012) https://doi.org/10.1117/12.918604
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Ninghang Hu, Henri Bouma, and Marcel Worring "Tracking individuals in surveillance video of a high-density crowd", Proc. SPIE 8399, Visual Information Processing XXI, 839909 (7 May 2012); https://doi.org/10.1117/12.918604

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