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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yi Song Shuxiao Li Jinglan Zhang Hongxing Chang |
| Copyright Year | 2014 |
| Description | Author affiliation: Integrated Inf. Syst. Res. Center, Inst. of Autom., Beijing, China (Yi Song; Shuxiao Li; Hongxing Chang) || Queensland Univ. of Technol., Brisbane, QLD, Australia (Jinglan Zhang) |
| Abstract | The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency. |
| Starting Page | 2233 |
| Ending Page | 2238 |
| File Size | 799931 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.388 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Histograms Feature extraction Image color analysis Real-time systems Image sequences scale adaptation object tracking mean shift feature point matching |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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