
Proceedings Paper
Tracking individuals in surveillance video of a high-density crowdFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 8399:
Visual Information Processing XXI
Mark Allen Neifeld; Amit Ashok, Editor(s)
PDF: 8 pages
Proc. SPIE 8399, Visual Information Processing XXI, 839909 (7 May 2012); doi: 10.1117/12.918604
Show Author Affiliations
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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