| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 96 === Currently, object tracking system is the core of the application of the hu-man-machine vision system for entertainment as well as surveillance system. Object tracking system is an important topic, but it wastes a lot of computing resource; how-ever, it has been used widely when computing has been speeding up in the past a few years. Many object tracking systems adopt Particle Filter and Mean Shift since they are easy to be implemented. However, Mean Shift results in conspicuous cumulative errors; and Particle Filter has serious drifting problem when the sample numbers are small. Accordingly, we propose a new algorithm－ Particle Filter Embedded Three-Step Search (PFETSS) to solve the drifting problem. Also by considering the color weights, our algorithm is able to acquire a robust result. The color features are needed to generate the target object model. However, the colors with large center gravity movement are excluded from our model. Our algorithm generates the color weights using object template, its color distribution of adjacent pixels, as well as the center gravity of colors. In the process of calculating their similarity, we take the major color of the target object into consideration. After Particle Filter, an approximate object position is obtained. Then a Three-Step Search algorithm is applied to solve drifting problem. The experimental results show that our proposed algorithm can not only solve the drifting problem of Particle Filter, but also require fewer samples. In our method, the probability of successful tracking is greatly improved by considering color weights in clutter background. We use 30 samples in our PFETSS. After 100 expe-riments, our experimental results show that out method can 100% track the object successfully. Particle Filter, on the other hand, has 73% of probability of success, other conditions remaining the same; As for computation time, Particle Filter Embedded Three-Step Search algorithm, with 30 samples, is 29.621 times faster than Full Search algorithm according to my experiment result. With the sample number, Particle Filter is 31.416 times faster than Full Search algorithm. Hence, the computation time between Particle Filter Embedded Three-Step Search algorithm and Particle Filter algorithm is not significantly different.
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