Fast Tracking Via Context Depth Model Learning
2015 IEEE International Conference on Image Processing (ICIP)(2015)
Abstract
Visual tracking is one of the challenging tasks in computer vision. In this paper, we propose a fast and robust visual tracking algorithm which is directly extended from STC [1]. By exploring RGB-D data, we construct a context depth model to record spatial correlation between the low-level features from the target and its surrounding regions. According to the continuity and stability of target in depth image, we adopt region growing method and a model updating schema for scaling and occlusion detection. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.
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Key words
fast tracking,context depth model learning,visual tracking,computer vision,STC,RGB-D data,spatial correlation,occlusion detection
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