Journal of Xidian University ›› 2021, Vol. 48 ›› Issue (2): 197-204.doi: 10.19665/j.issn1001-2400.2021.02.025

• Computer Science and Technology • Previous Articles     Next Articles

Cross-camera moving target tracking algorithm based on sparse representation

LU Yan1(),LIAO Guisheng1,2(),HUANG Qingxiang1()   

  1. 1. College of Energy Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,China
    2. School of Electronic Engineering,Xidian University,Xi’an 710071,China
  • Revised:2020-12-30 Online:2021-04-20 Published:2021-04-28


Cross-camera target tracking is very challenging,mainly because of the difference in the background area under different cameras and the randomness of the target movement behavior trajectory,which will accumulate interference errors very easily,and affect the matching accuracy,thus leading to the tracking failure.Aiming at this problem,a model of moving target tracking based on sparse representation is proposed in this paper.The model uses the difference in background brightness between different cameras to compensate the illumination of the target,so as to obtain a stable template matrix.At the stage of model solution,to solve the problem that the traditional greedy algorithm has a single atom matching pattern,ignoring the relationship between inner atoms and leading to a low reconstruction accuracy,the model adopts the band exclusion(BE) method in the band exclusion local optimization orthogonal matching pursuit(BLOOMP) algorithm to reduce the interatomic coherence.In addition,combining the local optimization(LO) technique with the new coherence discrimination mechanism,we obtain a more compact correlation band to update the support set,leading to improving the reconstruction accuracy.At the stage of template updating,in order to enhance the real time performance of the template matrix,the model uses correlation band and different weight coefficients as the template replacement mechanism.Simulation results show that the proposed method can track the interested target stably and robustly compared with the traditional algorithm under the condition of indoor and outdoor scenes.

Key words: across cameras, sparse representation, illumination compensation, target tracking, template update

CLC Number: 

  • TP391