Person re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Despite recent remarkable progress, person re-identification methods are either subject to the power of feature representation, or give equal importance to all examples. To mitigate these issues, we introduce a simple, yet effective, Multi-level and Multi-scale Horizontal Pooling Network (MMHPN) for person re-identification. Concretely, our contributions are three-fold:1) we take partial feature representation into account at different pooling scales and different semantic levels so that various partial information is obtained to form a robust descriptor; 2) we introduce a Part Sensitive Loss (PSL) to reduce the effect of easily classified partition to facilitate training of the person re-identification network, 3) we conduct extensive experimental results using the Market-1501, DukeMTMC-reID and CUHK03 datasets and achieve mAP scores of 83.4%, 75.1% and 65.4% respectively on these challenging datasets.
Bibliographical noteFunding Information:
This work is supported by National Natural Science Foundation of China (No. 61973066, 61471110), Foundation Project of National Key Laboratory (6142002301, 61420030302), the Distinguished Creative Talent Program of Shenyang(RC170490) and the Fundamental Research Funds for the Central Universities (N172608005, N182608004).
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- Horizontal pooling network
- Multi-level and multi-scale
- Part sensitive loss
- Person re-identification