| Summary: | 碩士 === 國立中央大學 === 電機工程學系 === 104 === In recent years, with the popularity of the camera and monitor, object detection and object tracking field are important and challenging research topic. For object tracking, it is difficult to track objects in complex environments. In order to improve the tracking speed and solve the shadowing problem, this paper uses Position Mutated Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PMHPSO-TVAC) algorithm for object tracking in real time. In terms of object detection, in this study, the background subtraction is used. And can cut out complete targets. The background subtraction has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target. Then, for model building, color histograms are used to build target models. However, in no-light environment, we can’t track any target, so this paper construct depth histogram object model to compensate for object model. Finally, we used the depth histogram and color histogram model with PMHPSO-TVAC algorithms for multi-target tracking.
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