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In this paper we propose a robust sparse based visual tracking method by exploiting local representations in a particle filter framework. We construct a Multi-level Local Dictionary which consists of positive templates and negative templates for discriminative model, Which divide the positive and negative dictionary into two levels called static templates and dynamic templates, respectively, thus...
Object recognition is a wide applied task in computer vision. Many fine-grained object recognition approaches are proposed in recent years to detect the same species objects effectively at subordinate-level. In this paper, we present a novel fine-grained vehicle recognition by utilizing collaborative feedback scheme of detection-classification-tracking in surveillance video. We collect a labeled data...
In this paper, we propose a novel collaborative appearance model for robust human tracking by exploiting both object and motion information in the bayesian framework. In contrast to most existing methods which use low or high-level visual cues, we use mid-level visual cues via superpixel with sufficient structure information to represent the object. In our work, the collaborative appearance is modeled...
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