Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 181-186.doi: 10.11896/JsJkx.190500093

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight

CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang   

  1. School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
  • Published:2020-07-07
  • About author:CHENG Zhong-Jian.His main research interests include data mining and so on.
    ZHOU Shuang-e, professor, is a senior member of China Computer Federation.Her main research interests include big data analysis technology, computer application technology, information secu-rity.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61370002).

Abstract: Target tracking is an important research field in computer vision.It is widely used in many aspects such as traffic navigation,autonomous driving and robotics.The generative model algorithm ASLA based on local sparse representation is fast and has high tracking accuracy,but it often loses its target in the face of complex tracking environment,such as target partial occlusion and dramatic change of target appearance.This paper analyzes the tracking principle of the original algorithm to get the cause of target tracking loss.Based on the ASLA algorithm,a three-point improvement method is proposed.1) Adaptive tracking of the target area size using multi-scale blocking method to obtain complementary target local information.2) In the feature pooling process of ASLA,block adaptive weight is modeled based on block reconstruction error to distinguish the discrimination information contained in different blocks,and introducing target occlusion information at different scales as weights in multi-scale pooling features.3)When the template is updated,the weight of the latest tracking results in subspace sparse representation is enhanced to make the updated template more similar to the recent tracking results,and improve the robustness of the algorithm.Experimental results show that the algorithm has higher tracking accuracy than algorithms such as ASLA in complex tracking environment,and can track the target in real time and accurately.

Key words: Target tracking, Multi-scale adaptive weight, Sparse representation, ASLA, Generative

CLC Number: 

  • TP391.41
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