- Tracking individuals in surveillance video of a high-density crowd
- Proceedings of SPIE, the International Society for Optical Engineering
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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.
- go to publisher's site
- Proceedings title: Visual Information Processing XXI
Place of publication: Bellingham, WA
Editors: M.A. Neifeld, A. Ashok
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