Computer Science ›› 2017, Vol. 44 ›› Issue (3): 300-306.doi: 10.11896/j.issn.1002-137X.2017.03.061

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Real-time L1-tracker Based on Haar-like Features

YAN Gang, QU Gao-chao and YU Ming   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to solve the problem of real-time in L1-tracker algorithm under the framework of particle filter,this paper proposed an improved L1-tracker algorithm based on Haar-like features.Firstly,the complete dictionary is reconstructed by the Haar-like features and feature blocks.The single pixels are replaced by the pixel blocks to make up positive and negative trivial templates.Then,the dimensions of over-complete dictionary are reduced by sparse representation and the calculation amount of the sparse matrices is significantly reduced.Secondly,the number of the target template is reduced in order to decrease the calculation of sparse representation.Finally,the updating frequency of the templates is controlled by experiential value.Experimental results demonstrate that the proposed algorithm can significantly improve real-time of L1-tracker algorithm and is effective under short time occlusion,the deformation of target and illumination changes.

Key words: L1-tracker,Particle filter,Sparse representation,Object tracking,Haar-like features

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