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Proceedings Paper

Robust tracking of people in crowds with covariance descriptors
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Paper Abstract

In order to control riots in crowds, it is helpful to get the ringleader under control. A great support to achieve this task is the capability to automatically track individual persons in a video sequence taken from a crowd. In this paper we address the robustness of such a tracking function. We start from the results of a previous evaluation of tracking methods, where a so-called Covariance-Tracker was found to be most appropriate. This tracker uses covariance matrices as object descriptors, as proposed by Porikli et al. The set of all covariance matrices describes a Riemannian manifold that is used to compare and update the covariance descriptors during tracking. We propose Covariance-Tracker adaptations to improve its performance. Furthermore, we summarize the performance evaluation results of the original method and compare these with the results of the adapted one. The result is a robust method for tracking people in crowds which can improve situational awareness.

Paper Details

Date Published: 27 April 2009
PDF: 9 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410T (27 April 2009); doi: 10.1117/12.820067
Show Author Affiliations
Jürgen Metzler, Fraunhofer Institute for Information and Data Processing (Germany)
Dieter Willersinn, Fraunhofer Institute for Information and Data Processing (Germany)


Published in SPIE Proceedings Vol. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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