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Content Provider | IEEE Xplore Digital Library |
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Author | Branson, K. Belongie, S. |
Copyright Year | 2005 |
Description | Author affiliation: Dept. of Comput. Sci. & Eng., UC San Diego, La Jolla, CA, USA (Branson, K.; Belongie, S.) |
Abstract | We present a particle filtering algorithm for robustly tracking the contours of multiple deformable objects through severe occlusions. Our algorithm combines a multiple blob tracker with a contour tracker in a manner that keeps the required number of samples small. This is a natural combination because both algorithms have complementary strengths. The multiple blob tracker uses a natural multi-target model and searches a smaller and simpler space. On the other hand, contour tracking gives more fine-tuned results and relies on cues that are available during severe occlusions. Our choice of combination of these two algorithms accentuates the advantages of each. We demonstrate good performance on challenging video of three identical mice that contains multiple instances of severe occlusion. |
Sponsorship | IEEE Comput. Soc |
Starting Page | 1039 |
Ending Page | 1046 |
File Size | 610998 |
Page Count | 8 |
File Format | |
ISBN | 0769523722 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2005.349 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2005-06-20 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Mice Computer vision Video sequences Computer science Filtering algorithms Robustness Particle tracking Biomedical monitoring Computerized monitoring Animals |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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