Weighted Sub-block Mean-Shift Tracking with Improved Level Set Target Extraction
Wang Xingmei, Dong Hongbin, Chu Yan, Li Lin
Journal of Information & Systems Management, Vol. 5, No. 3, 2015.
DOI: 
Link: http://www.dline.info/jism/v5n3ed.pdf
Abstract:
Mean-shift tracking algorithm is a widely used tool for efficiently tracking target. However, the background
change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel Mean-shift
tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of
each sub-block is determined by the similarity of target sub-block and candidate sub-block, also by the ratio of the target
sub-block area and the overall area. At the same time, the target sub-block area is found by means of the narrow band level
set combined with penalty to improve the extraction accuracy and operating efficiency. Both of the target regionâ€™s RGB color
information and the pixelâ€™s position information are taken into consideration while describing the feature model of target
and candidate region inside each sub-block. Many experimental results demonstrate the successful tracking of targets with
background change and shade during the dynamic scene, where the basic Mean-shift tracking algorithm fails. And the
proposed method has better tracking performance with higher tracking accuracy and adaptability.