Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICPR.2006.1131
Title: Tracking a variable number of human groups in video using probability hypothesis density
Authors: Wang, Y.-D.
Wu, J.-K.
Kassim, A.A. 
Huang, W.-M.
Issue Date: 2006
Source: Wang, Y.-D.,Wu, J.-K.,Kassim, A.A.,Huang, W.-M. (2006). Tracking a variable number of human groups in video using probability hypothesis density. Proceedings - International Conference on Pattern Recognition 3 : 1127-1130. ScholarBank@NUS Repository. https://doi.org/10.1109/ICPR.2006.1131
Abstract: We apply a multi-target recursive Bayes filter, the Probability Hypothesis Density (PHD) filter, to a visual tracking problem: tracking a variable number of human groups in video. First, we use background subtraction to detect human groups which appear as foreground blobs. The PHD filter is implemented using sequential Monte Carlo methods; and the centroids of the foreground blobs are used as the measurements to update the PHD filter. Our experimental results show that when human groups appear, merge, split, and disappear in the field of view of a camera, our method can track them correctly. © 2006 IEEE.
Source Title: Proceedings - International Conference on Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/72061
ISBN: 0769525210
ISSN: 10514651
DOI: 10.1109/ICPR.2006.1131
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