CamShift guided particle filter for visual tracking

Authors:
Zhaowen Wang;Xiaokang Yang;Yi Xu;Songyu Yu
Affiliations:
Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, RM 409, No. 5, DianYuan Building, Dongchuan Road 800, Shanghai 200240, PR China;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, RM 409, No. 5, DianYuan Building, Dongchuan Road 800, Shanghai 200240, PR China;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, RM 409, No. 5, DianYuan Building, Dongchuan Road 800, Shanghai 200240, PR China;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, RM 409, No. 5, DianYuan Building, Dongchuan Road 800, Shanghai 200240, PR China
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

In this article, a novel algorithm - CamShift guided particle filter (CAMSGPF) - is proposed for tracking object in video sequence. CamShift is incorporated into the probabilistic framework of particle filter as an optimization scheme for proposal distribution. Meanwhile, in the context of particle filter, the scale adaptation of CamShift is improved and the computation complexity is reduced. It is demonstrated through several real tracking tasks that the new method performs better than baseline trackers in both tracking robustness and computational efficiency.