Robust tracking algorithm using mean-shift and particle filter

Jianhua Wang; Wei Liang

doi:10.1117/12.920227

13 January 2012 Robust tracking algorithm using mean-shift and particle filter

Jianhua Wang, Wei Liang

Proceedings Volume 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies; 83502J (2012) https://doi.org/10.1117/12.920227
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

Abstract

Aiming to the problems that Mean-Shift algorithm costs low computation, but easy to fall into local maximum, and huge computation of Particle Filter tracking algorithm leads to low real-time processing capacity, according to the need of real stereo vision measurement system, a kind of tracking algorithm which combines Mean-Shift and Particle Filter by essentiality function is proposed. Under the condition without occlusion, Mean-Shift is used to track object. When object is occluded, Particle Filter is applied to accomplish the later object tracking. These two algorithms alternate by a defined threshold. The tracking algorithm is used into real stereo vision measurement system. Experiment result indicates that the algorithm takes on high efficiency, so it is of high practicability.

Citation Download Citation

Jianhua Wang and Wei Liang "Robust tracking algorithm using mean-shift and particle filter", Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83502J (13 January 2012); https://doi.org/10.1117/12.920227

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