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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Aeschliman, C. Park, J. Kak, A.C. |
| Copyright Year | 2010 |
| Description | Author affiliation: Purdue University (Aeschliman, C.; Park, J.; Kak, A.C.) |
| Abstract | Most tracking algorithms implicitly apply a coarse segmentation of each target object using a simple mask such as a rectangle or an ellipse. Although convenient, such coarse segmentation results in several problems in tracking — drift, switching of targets, poor target localization, to name a few — since it inherently includes extra non-target pixels if the mask is larger than the target or excludes some portion of target pixels if the mask is smaller than the target. In this paper, we propose a novel probabilistic framework for jointly solving segmentation and tracking. Starting from a joint Gaussian distribution over all the pixels, candidate target locations are evaluated by first computing a pixel-level segmentation and then explicitly including this segmentation in the probability model. The segmentation is also used to incrementally update the probability model based on a modified probabilistic principal component analysis (PPCA). Our experimental results show that the proposed method of explicitly considering pixellevel segmentation as a part of solving the tracking problem significantly improves the robustness and performance of tracking compared to other state-of-the-art trackers, particularly for tracking multiple overlapping targets. |
| Starting Page | 1371 |
| Ending Page | 1378 |
| File Size | 2220190 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539810 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Radar tracking Pixel Image segmentation Image generation Gaussian distribution Distributed computing Principal component analysis Robustness Particle measurements |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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