JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (3): 14-22.doi: 10.6040/j.issn.1672-3961.0.2015.316

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Object tracking via L1 norm and least soft-threshold square

WANG Haijun1,2, GE Hongjuan1, ZHANG Shengyan2   

  1. 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China;
    2.Key Laboratory of Aviation Information Technology in University of Shandong, Binzhou University, Binzhou 256603, Shandong, China
  • Received:2015-04-07 Online:2016-06-30 Published:2015-04-07

Abstract: Due to the occlusion and motion blur in the traditional object tracking algorithm, a novel object tracking algorithm via L1 norm and least soft-threshold square was proposed to solve the problem of the failure of object tracking based on sparse representation. Firstly, the appearances of the object were modeled by the PCA(Principal Component Analysis)basis vector and the representation coefficients were constrained by L1 norm. Secondly, the trivial error was solved by the least soft-threshold square and the occlusion factor was taken account in the updation of the observation model. At last, the object tracking algorithm was developed in the Bayesian inference framework. Experiments were conducted on fourteen challenging videos and the experimental results showed that the proposed algorithm could cope well with the occlusion, angle variation, scale variation and illumination variation, with the higher average overlap rate and the lower average center point error, compared with the other tracking algorithm.

Key words: sparse representation, object tracking, L1 norm, least soft-threshold square, observation model

CLC Number: 

  • TP391
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