NDLI: A Robust Object Tracking Approach using Mean Shift

Content Provider	IEEE Xplore Digital Library
Author	Zhiqiang Wen Zixing Cai
Copyright Year	2007
Description	Author affiliation: Central South Univ., Changsha (Zhiqiang Wen; Zixing Cai)
Abstract	Background pixels in object model will increase localization error of object tracking, but in order to let the object contained in object model, it is inevitable to introduce some background pixels in object model. For reducing the localization error of object tracking, a straightforward approach is to omit the background pixels when the kernel histogram of object model is being computed, but there are many knotty problems for it. A weight parameter integrating background features is used in object model in this paper. The weight parameter indicates the similarity between background feature and object feature and can reduce localization error of object tracking. The experimental results show our approach has good localization precision of object tracking, and is robust against occlusion.
Starting Page	170
Ending Page	174
File Size	316869
Page Count	5
File Format	PDF
ISBN	9780769528755
DOI	10.1109/ICNC.2007.132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Information science Clustering methods Clustering algorithms Computer errors Educational institutions Density functional theory Robustness Electronic mail Kernel
Content Type	Text
Resource Type	Article

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