NDLI: Combining Mean-Shift and Particle Filter for Object Tracking

Content Provider	IEEE Xplore Digital Library
Author	Da Tang Yu-Jin Zhang
Copyright Year	2011
Abstract	Mean-shift is an effective algorithm for object tracking. However, it has a poor performance when the illumination condition changes fast or the tracking target being shadowed. By contract, particle filter based object tracking has a better tracking performance, but the tracking speed is much slower compared to mean-shift. Owing to the limitations of just using a single algorithm, a novel object tracking method based on both mean-shift and particle filter is proposed in this paper. A system with feedback has been constructed by the proposed method, in which the mean-shift technique is used for initial registration, and the particle filter is called to improve the performance of tracking when the tracking result with mean-shift is unconvincing. RGB color histogram is exploited as image feature and Bhattacharyya coefficient is used for measuring the similarity between object model and candidate regions. Tracking experiments evaluated on various videos show that the proposed method is well-behaved in cases that objects have shift-variant, rotation and scaling, and achieves a satisfying tracking speed.
Starting Page	771
Ending Page	776
File Size	1220468
Page Count	6
File Format	PDF
ISBN	9781457715600
DOI	10.1109/ICIG.2011.118
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Target tracking Image color analysis Color Quantization object tracking mean-shift Particle filters particle filter algorithm fusion Videos
Content Type	Text
Resource Type	Article

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