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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tiesheng Wang Gu, I.Y.H. Khan, Z.H. Pengfei Shi |
| Copyright Year | 2009 |
| Description | Author affiliation: Dept. of Signals and Systems, Chalmers Univ. of Technology, Sweden (Tiesheng Wang; Gu, I.Y.H.; Khan, Z.H.) || Institute of IPPR, Shanghai Jiao Tong University, Shanghai, China (Pengfei Shi) |
| Abstract | This paper addresses issues on moving object tracking from videos. We propose a novel tracking scheme that jointly exploits local object features using consensus point correspondences, and global object appearance and shape models using adaptive particle filter-based eigen-tracking. The paper include the following main novelties: (a) employ consensus feature point correspondences to estimate the motion vector of shape model; (b) employ adaptive particle filters and motioncorrected state vector for joint appearance- and shape-based eigen-tracking. An adaptive number of particles is chosen automatically based on an updated estimation of covariancematrix. Further, online learning is made adaptive to avoid learning using partially-occluded objects. The proposed scheme is realized by integrating SURF and RANSAC [8, 9] for estimating consensus point correspondences, and modify an existing particle filter-based eigen-tracking [4]. Experiment results on tracking moving objects in videos have shown that the proposed scheme provides more accurate tracking, especially for objects with fast motion or long-term partial occlusions. The average number of particles is significantly reduced. Comparisons have been made with an existing method, results have shown that the proposed scheme has provided an improved tracking accuracy at the cost of more computations. |
| Starting Page | 1370 |
| Ending Page | 1373 |
| File Size | 877312 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424442904 |
| ISSN | 19457871 |
| DOI | 10.1109/ICME.2009.5202758 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle filters Particle tracking Principal component analysis Videos Shape Adaptive filters Motion estimation State estimation Covariance matrix Computational efficiency consensus point correspondences visual object tracking adaptive particle filters PCA SURF |
| Content Type | Text |
| Resource Type | Article |
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