A coarse-to-fine kernel matching approach for mean-shift based visual tracking |
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Authors: | L Liangfu F Zuren C Weidong J Ming |
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Institution: | (1) Xi’an Institute of Applied Optics, PO Box 123, Xi’an, 710065, China;(2) System Engineering Institute, Xi’an Jiaotong University, Xi’an, 710049, China;(3) College of Computer Science, Shaanxi Normal University, Xi’an, 710062, China |
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Abstract: | Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform
whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize
rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate
template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper
researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object,
we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very
large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value
by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking
will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of
motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully
realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared
with background-weighted histogram algorithm in the literature. |
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Keywords: | visual tracking coarse-to-fine kernel matching mean shift |
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