Human Tracking Using Sequential Importance Sampling Particle Filter by Omnidirectional Camera
碩士 === 臺灣大學 === 電機工程學研究所 === 95 === Visual tracking is an important topic in computer vision and robotics fields. The omnidirectional cameras provide a wider filed of view, but tracking with an omnidirectional camera must overcome the warping and low resolution drawback. This thesis presents an appr...
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| Format: | Others |
| Language: | en_US |
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2007
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| Online Access: | http://ndltd.ncl.edu.tw/handle/91727974431192058803 |
| Summary: | 碩士 === 臺灣大學 === 電機工程學研究所 === 95 === Visual tracking is an important topic in computer vision and robotics fields. The omnidirectional cameras provide a wider filed of view, but tracking with an omnidirectional camera must overcome the warping and low resolution drawback. This thesis presents an approach based on the sequential importance sampling particle filter framework to track multiple humans using an omnidirectional camera. In order to efficiently converge to the target distribution, a foreground-based importance sampling mechanism using foreground segmentation algorithm is proposed to draw particles from currently observed image. The fusion of color and contour features to evaluate the likelihood measurement makes human tracking more accurate. Likelihood evaluation by integrating two-space enhances the robustness of the system to the warping effect. The overall performance is validated using several videos in the experiments.
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