Visual object tracking is an important computer vision task which can be applied to many domains such as visual surveillance, robotics, media production and augmented reality. Despite extensive research on this topic in recent decades, achieving robust tracking performance still remains a huge challenge regarding abrupt motions, occlusions, changing appearance patterns of the object and background interference, etc. Visual attention is one of the key mechanisms of human visual system which directs the processing resources to the visual data of the potentially most relevant, specially directs our gaze rapidly towards objects of interest in our visual environment and as a result humans can easily achieve stable object tracking. Therefore, introducing the visual attention mechanism to the object tracking in complex scenes to achieve stable and humanoid tracking algorithms, has important academic significance and application value. This thesis focuses on visual attention based tracking methods. This research was partly supported by National Natural Science Foundation of China (61210009, 61100098, 61379097). The key research contents and contributions of this thesis can be summarized as follows: (1) The state-of-the-art visual attention based methods for tracking are reviewed. The attention-based visual tracking algorithms are classified into five categories and detailed descriptions of representative methods in each category are provided, and their pros and cons are examined. Besides, we highlight the advantages of attention-based tracking methods and provide insights for future. (2) To cope with the problem that how to direct the gaze to salient regions where target may appears, we propose a new computational model, i.e., top-down frequency analysis visual attention model which first introduces top-down information to frequency analysis attention model and can direct the processing resources to the salient regions which are most relevant to the target need to pop out. (3) We present a novel spectral analysis visual attention based object tracking method. For simulating top-down visual attention mechanism and calculating saliency maps, we first introduce top-down information to spectral analysis attention model. After calculating saliency maps, local target search and global target search are performed based on these saliency maps. The proposed method can cope with very difficult situations including abrupt motion and target reappearing after longtime oc...
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