Camshift Guided Particle Filter for Visual Tracking


Particle filter and mean shift are two important methods for tracking object in video sequence, and they are extensively studied by researchers. As their strength complements each other, some effort has been initiated in [1] to combine these two algorithms, on which the advantage of computational efficiency is focused. In this paper, we extend this idea by exploring even more intrinsic relationship between mean shift and particle filter, and propose a new algorithm, CamShift guided particle filter (CAMSGPF). In CAMSGPF, two basic algorithms - CamShift and particle filter - can work cooperatively and benefit from each other, so that the overall performance is improved and some redundancy in algorithms can be removed. Experimental results show that the proposed method can track objects robustly in various environments, and is much faster than the existing methods.

DOI: 10.1016/j.patrec.2008.10.017

Extracted Key Phrases

5 Figures and Tables


Citations per Year

57 Citations

Semantic Scholar estimates that this publication has 57 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Wang2007CamshiftGP, title={Camshift Guided Particle Filter for Visual Tracking}, author={Zhaowen Wang and Xiaokang Yang and Yi Xu and Songyu Yu}, journal={2007 IEEE Workshop on Signal Processing Systems}, year={2007}, pages={301-306} }