Video tracking of people under severe occlusions

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Video surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision and pattern recognition. The goal of this research is to develop a real-time automatic tracking system which is both reliable and efficient by utilizing computational approaches. The literature has presented many valuable methods on object tracking; however, most of those algorithms can only perform effectively under simple scenarios. There are a few algorithms which attempt to accomplish object tracking in a complex dynamic scene and have successfully achieved their goals when the dynamic scene is not too complex. However no system yet is capable of accurately handling object tracking, especially human tracking, in a crowded environment with frequent and continuous occlusions. Therefore, the goal of my research is to develop an effective human tracking algorithm which takes into account and overcomes the various factors involved in a complex dynamic scene. The founding idea is that of dividing the human figure into five main parts, and track each individually under a constraint of integrity. Data association in new frames is performed on each part, and is inferred for the whole human figure through a fusion rule. This approach has proved a good trade off between model complexity and actual computability. Experimental results have confirmed the effectiveness of the methodology.
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