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Proceedings Paper

Tracking individuals in surveillance video of a high-density crowd
Author(s): Ninghang Hu; Henri Bouma; Marcel Worring
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Paper 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.

Paper Details

Date Published: 7 May 2012
PDF: 8 pages
Proc. SPIE 8399, Visual Information Processing XXI, 839909 (7 May 2012); doi: 10.1117/12.918604
Show Author Affiliations
Ninghang Hu, TNO (Netherlands)
Univ. of Amsterdam (Netherlands)
Henri Bouma, TNO (Netherlands)
Marcel Worring, Univ. of Amsterdam (Netherlands)

Published in SPIE Proceedings Vol. 8399:
Visual Information Processing XXI
Mark Allen Neifeld; Amit Ashok, Editor(s)

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