Tracking individuals in surveillance video of a high-density crowd
conference paper
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.
TNO Identifier
460333
Publisher
SPIE
Article nr.
839909
Source title
Visual Information Processing XXI, 24 April 2012, Baltimore, MD, USA
Editor(s)
Neifeld, M.A.
Ashok, A.
Ashok, A.
Place of publication
Bellingham, WA