Sparse Acceleration Algorithm Based on Likelihood Upper Bound
It is a great challenge for intelligent analysis and discovery of useful information from a great massive video resources, and the target tracking become more and more popular as the important part of the intelligent video surveillance. The feature extracted by the traditional method cannot keep the invariance of the target when the target surfers occlusion, illumination and pose variation in the moving process, which leads to the failure of the tracker. To handle the problems mentioned above, we conduct a survey on the target tracking based on sparse representation. In view of the problem that a large amount of time is needed to solve the problem of sparse expression optimization, we propose an acceleration algorithm based on the observation likelihood upper bound. The effectiveness of the proposed algorithm is verified by experiments on a number of standard test sets.
KeywordsSparse acceleration algorithm Sparse representation Likelihood upper bound
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