
Proceedings Paper
Tracking people in mixed modality systemsFormat | Member Price | Non-Member Price |
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Paper Abstract
In traditional surveillance systems tracking of objects is achieved by means of image and video processing. The
disadvantages of such surveillance systems is that if an object needs to be tracked - it has to be observed by
a video camera. However, geometries of indoor spaces typically require a large number of video cameras to
provide the coverage necessary for robust operation of video-based tracking algorithms. Increased number of
video streams increases the computational burden on the surveillance system in order to obtain robust tracking
results. In this paper we present an approach to tracking in mixed modality systems, with a variety of sensors.
The system described here includes over 200 motion sensors as well as 6 moving cameras. We track individuals
in the entire space and across cameras using contextual information available from the motion sensors. Motion
sensors allow us to almost instantaneously find plausible tracks in a very large volume of data, ranging in months,
which for traditional video search approaches could be virtually impossible. We describe a method that allows us
to evaluate when the tracking system is unreliable and present the data to a human operator for disambiguation.
Paper Details
Date Published: 29 January 2007
PDF: 11 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080L (29 January 2007); doi: 10.1117/12.714078
Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)
PDF: 11 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080L (29 January 2007); doi: 10.1117/12.714078
Show Author Affiliations
Yuri Ivanov, Mitsubishi Electric Research Labs. (United States)
Alexander Sorokin, Univ. of Illinois, Urbana Champaign (United States)
Alexander Sorokin, Univ. of Illinois, Urbana Champaign (United States)
Christopher Wren, Mitsubishi Electric Research Labs. (United States)
Ishwinder Kaur, Massachusetts Institute of Technology (United States)
Ishwinder Kaur, Massachusetts Institute of Technology (United States)
Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)
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