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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yu Huang Llach, J. Chao Zhang |
| Copyright Year | 2008 |
| Description | Author affiliation: State Key Lab. of Machine Perception, Peking Univ., Beijing (Chao Zhang) || Thomson Corp. Res., Princeton, NJ (Yu Huang; Llach, J.) |
| Abstract | In this paper an efficient method of small object localization is proposed that integrates detection and tracking. The system is initialized using a strong detector and then it locates the object over time using a weak detector and a temporal tracker. Both of strong and weak detectors are based on foreground-background segmentation. The strong detector is created from shape analysis of foreground blobs and used to trigger the object tracker. The weak detector is built with outputs from the foreground detection likelihood and integrated into the trackerpsilas observation likelihood. In the particle filter-based object tracker, motion estimation is embedded to generate a better proposal distribution and a mixture model is tailored to handle the ambiguity of template matching due to cluttered background. As a case study, the proposed scheme is applied to ball detection and tracking in soccer game videos. Promising results are presented to illustrate the proposed method effectively handles heavy clutter, occlusion and motion blur. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 479624 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424421749 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2008.4761480 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-08 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Object detection Particle tracking Particle filters Detectors Shape Matched filters Motion estimation Proposals Games Videos |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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