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Sparse Acceleration Algorithm Based on Likelihood Upper Bound

  • Pan LiuEmail author
  • Fenglei Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1084)

Abstract

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.

Keywords

Sparse acceleration algorithm Sparse representation Likelihood upper bound 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National University of Defence TechnologyChangshaChina

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