Person Re-Identification by Discriminative Local Features of Overlapping Stripes

Symmetry

Fawad¹  Muhammad Jamil Khan¹  MuhibUr Rahman² 

[1] ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan;Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada;

关键词: re-identification;    texture;    tracking;    convolution;    filter;   

DOI  :  10.3390/sym12040647

来源: DOAJ

【 摘 要 】

The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance system. The proposed DLFOS model accumulates the discriminative features from the local patch of each overlapping strip of the pedestrian appearance. The concatenation of histogram of oriented gradients, Gaussian of color, and the magnitude operator of CJLBP bring robustness in the final feature vector. The experimental results show that our proposed feature extraction model achieves rank@1 matching rate of 47.18% on VIPeR, 64.4% on CAVIAR4REID, and 62.68% on Market1501, outperforming the recently reported models from the literature and validating the advantage of the proposed model.

【 授权许可】

Unknown