Contribution-Based Multi-Stream Feature Distance Fusion Method With ${k}$ -Distribution Re-Ranking for Person Re-Identification

Person re-identification (re-ID) is an important and challenging topic in video surveillance and public security. Re-ID aims to retrieve persons from different cameras. Despite the developments in recent years, re-ID still faces many challenges due to different camera views, changeable person posture, complex background, and occlusion. To exploit more discriminative image similarity descriptor, we propose a novel method in this paper. First, we design a body partition extraction network to extract three body regions with efficient alignment. Second, we propose a multi-stream contribution framework to fuse feature distance with different contributions and generate the final image similarity descriptor. In addition, we combine re-ID and semantic segmentation. A mask feature is introduced to the proposed framework and we design a contribution feedback module to generate contribution coefficients dynamically. Third, in order to improve re-ID performance, we propose a fragment learning method to optimize the contribution feedback module. Fourth and last, we propose a
${k}$
-distribution re-ranking strategy to further improve performance. Our method achieves competitive results on two popular datasets, CUHK03, and Market1501, with rank-1 accuracy of 93.5% and 85.7%. The proposed re-ranking method achieves 2.3% and 2.8% performance boost. The data demonstrate the effectiveness of the proposed multi-stream contribution framework and the
${k}$
-distribution re-ranking strategy.