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Content Provider | IEEE Xplore Digital Library |
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Author | Zhao Yunji Zhang Bin Zhang Xinliang |
Copyright Year | 2014 |
Description | Author affiliation: Henan Mech. Electr. Vocational Coll., Zhengzhou, China (Zhang Bin) || Sch. of Electr. Eng. & Autom., Henan Polytech. Univ., Jiaozuo, China (Zhao Yunji; Zhang Xinliang) |
Abstract | An adaptive model update mechanism for mean shift tracking is proposed in this paper. Gaussian Model always has been used in background model estimation to realize object tracking. It is novel that each bins of kernel histogram is modeled as mixture of Gaussian and the on-line approximation used to update the model. Gaussian distributions are ordered based on the fitness value of weight and covariance. Object model is determined by each Gaussian distributions and weight. Therefore the improved mean shift can not only update object model in time but also deal with object appearance changes and occlusion. Experiments demonstrate that the improved method can track objects under the changes of appearance and occlusion with satisfactory results. |
Starting Page | 4831 |
Ending Page | 4835 |
File Size | 416446 |
Page Count | 5 |
File Format | |
ISBN | 9789881563873 |
ISSN | 19341768 |
DOI | 10.1109/ChiCC.2014.6895758 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-07-28 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | TCCT, CAA |
Subject Keyword | Histograms Adaptation models Mean shift Image color analysis Color Gaussian model Mathematical model Gaussian mixture model Model update |
Content Type | Text |
Resource Type | Article |
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