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The bag-of-feature model has become a state-of-the-art method of visual classification. Visual codebooks can be used to capture image statistical information for object detection and classification, which is extracted from local image patches and based on the quantization of robust appearance descriptors. In this paper, more information of target objects(More)
The problem of object tracking in dense clutter is a challenge in computer vision. This paper proposes a method for tracking object robustly by combining the online selection of discriminative color features and the offline selection of discriminative Haar features. Furthermore, the cascade particle filter which has four stages of importance sampling is(More)
The sparse representation has been widely used in many areas including visual tracking. The part-based representation performs outstandingly by using non-holistic templates to against occlusion. This paper combined them and proposed a robust object tracking method using part-based sparsity model for tracking an object in a video sequence. In the proposed(More)
Online learned tracking is widely used to handle the appearance changes of object because of its adaptive ability. Learning to rank technique has attracted much attention recently in visual tracking. But the tracking method with online learning to rank suffers from the error accumulation problem during the self-training process. To solve this problem, we(More)
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