| 英文摘要 | Object Tracking based on computer vision is important in scientific research and project. For the existence of relative motion between object and the camera, or the complexity of scenes, there may be many problems in video sequences such as changing illumination, serious debris or noise, partial or complete occlusions, or the object state may be changing from time to time. All of that make object tracking in various scenes difficult to be achieved. In view of these problems, this paper makes use of knowledge in mean-shift, particle filter, feature detection as well as the visual attention mechanism, to study the object tracking methods under dynamic scenes. The main contents and contributions of this paper are as follows: The advantages and shortcomings of conventional Mean-shift tracking algorithm and the bandwidth-adaptive Mean-shift algorithm are analyzed, and the object initialization methods are discussed. A run-list based Blob searching method is proposed to search the target globally once it is lost. An improved bandwidth-adaptive algorithm is proposed, then implementation steps are provided and at last experiments are carried out to check the property of this algorithm. The improved algorithm has advantages of bandwidth adaptive algorithm. It updates the object bandwidth through iteration, realizes automatic initialization of the object and searching of the missing object. None the above algorithm is robust to illumination changes, similar interference or occlusions. In view of that, a tracking algorithm called ABMSPF (Adaptive Bandwidth Mean-shift Method Assisted by Particle Filter) is proposed. This algorithm takes Mean-shift algorithm as a framework, combining with particle filter which is used to provide auxiliary positioning information, and the target state is determined by a data fusion strategy. Properties such as stability, location accuracy and calculating complexity of ABMSPF, improved bandwidth-adaptive Mean-shift and the particle filter methods are analyzed experimentally. The ABMSPF algorithm is proved to be able to track object accurately in complex scenes with illumination changing, occlusions or color interferences, and the tracking trajectory is smooth. Under situations such as image rotating, scale changing, illumination, or perspective changing, several commonly used feature detecting methods like Harris and SIFT are analyzed, the properties of robustness, stability and computing c... |
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