| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 95 === In this thesis, we propose the new mean-shift tracking algorithms based on a new similarity measure function. The joint spatial-color feature is used as our basic model elements. The target image is modeled with the kernel density estimation and we use the concept of expectation of the estimated kernel density to develop the new similarity measure functions. With these new similarity measure functions, two new similarity-based mean-shift tracking algorithms were derived. To enhance the robustness, we add the weighted-background information to the proposed mean-shift tracking algorithm. In order to solve the deformation problem, the principal component analysis method is used to update the orientation of the tracking object, and a simple method is elaborated to monitor the scale of the object. The results of the experiments show that the new similarity-based tracking algorithms are real-time and can track the moving object correctly, and update the orientation and scale of the object automatically.
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