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A probabilistic framework for segmentation and tracking of multiple non rigid objects for video surveillance

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2 Author(s)
A. Ivanovic ; Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA ; T. S. Huang

This paper presents a probabilistic framework for segmenting and tracking multiple non rigid foreground objects for video surveillance, using a static monocular camera. The algorithm combines information in a probabilistic sense and poses the problem of matching the segmented foreground objects with blobs in the next frame as a non bipartite matching problem. To solve this problem, probability is calculated for each possible matching. Initialization of new objects is also treated in a probabilistic manner. The new framework is shown to be able to handle a greater set of difficult situations and to improve performance significantly.

Published in:

Image Processing, 2004. ICIP '04. 2004 International Conference on  (Volume:1 )

Date of Conference:

24-27 Oct. 2004