| dc.description.abstract | In recent years, as cameras and monitors become more and more popular, object tracking becomes a hot issue. In order to improve the accuracy of the tracking object and solve the occlusion problem, in this thesis, the Artificial Bee Colony (ABC) algorithm is used for object tracking in real time.
In terms of object detection, in this thesis, the background subtraction is used for it can cut out complete targets, has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target and calculate its center. Then, for model building, color histograms are used to build target models. In order to avoid the interference of light, in this thesis, the HSV (Hue, Saturation and Value) color space is used. Moreover, for object tracking, in this thesis, the ABC algorithm which has a simple structure is used to find the best solution for it is easily used and its convergence is fast. Occlusion is always a big problem for object tracking. Therefore, in this thesis, the adaptive searching window is applied to exclude occlusion; the searching window will zoom in or out, depending on its fitness value. If the tracking window loses the targets, the searching window will increase. If the tracking window finds the targets, the searching window will adjust to the original size.
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