NDLI: Improved CAMShift object tracking based on non-linear kernel density estimation and Kalman filter

Content Provider	IEEE Xplore Digital Library
Author	Yang Wu Lihong Xu Dawei Li Yuan Xu
Copyright Year	2011
Description	Author affiliation: Dept. of Control Science and Engineering, Tongji University, Shanghai, China (Yang Wu; Lihong Xu; Dawei Li; Yuan Xu)
Abstract	This paper firstly chooses non-linear kernel density estimation for single moving object detection. The interest targets are obtained by labeling the connected region in the detected binary image. Kalman filter is then employed to proffer primary searching window for CAMShift algorithm to track target and predict the future position of the target. Meanwhile, the histogram of target is updated by the color information in the region obtained by periodical detection of non-linear kernel density estimation. This paper also puts forward a method aims at addressing the occlusion problem in tracking. Experimental results show that the strategy which combines the foreground detection, Kalman filter and CAMShift, can realize automatic and efficient tracking of moving target. The reliable performance of this algorithm satisfies the real-time requirement, and is robust against the effects of unstable scene illumination, and object occlusion.
Starting Page	1
Ending Page	7
File Size	1410206
Page Count	7
File Format	PDF
ISBN	9781457721656
e-ISBN	9781457721663
DOI	10.1109/ISPACS.2011.6146056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-07
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Target tracking occlusion handling target tracking kernel density estimation CAMShift Kalman filter histogram
Content Type	Text
Resource Type	Article

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