Human Re-identification Based on Part Segmentation

doi:10.16182/j.issn1004731x.joss.17-0180

Abstract

Abstract: Human re-identification is a difficult problem to solve in process of video analysis of non-overlapping multi-camera surveillance system. A new algorithm of human re-identification is proposed on the basis of human part segmentation. Based on the depth of bone points to achieve the human body segmentation, the optimal key frame is selected by using the scoring strategy for all parts of the same human multi-frame image segmentation; the different weights for the global color feature and the HOG feature are assigned; all the characteristics to establish a human target model are combined; and the EMD (Earth Mover’s Distance) distance is used to determine the similarity between the targets. The effectiveness is validated on Kinect REID and BIWI RGBD-ID datasets which show that the proposed method has strong robustness and higher recognition rate.

Key words: person re-identification, human segmentation, depth information, color characteristics, HOG characteristics

CLC Number:

TP391

Jiang Hua, Zhang Liang. Human Re-identification Based on Part Segmentation[J]. Journal of System Simulation, 2019, 31(6): 1085-1091.

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