Sequential End-to-end Network for Efficient Person Search

• URL: http://arxiv.org/abs/2103.10148v1
• Date: Thu, 18 Mar 2021 10:28:24 GMT
• Title: Sequential End-to-end Network for Efficient Person Search
• Authors: Zhengjia Li, Duoqian Miao
• Abstract summary: Person search aims at jointly solving Person Detection and Person Re-identification (re-ID) Existing works have designed end-to-end networks based on Faster R-CNN. We propose a Sequential End-to-end Network (SeqNet) to extract superior features.
• Score: 7.3658840620058115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person search aims at jointly solving Person Detection and Person Re-identification (re-ID). Existing works have designed end-to-end networks based on Faster R-CNN. However, due to the parallel structure of Faster R-CNN, the extracted features come from the low-quality proposals generated by the Region Proposal Network, rather than the detected high-quality bounding boxes. Person search is a fine-grained task and such inferior features will significantly reduce re-ID performance. To address this issue, we propose a Sequential End-to-end Network (SeqNet) to extract superior features. In SeqNet, detection and re-ID are considered as a progressive process and tackled with two sub-networks sequentially. In addition, we design a robust Context Bipartite Graph Matching (CBGM) algorithm to effectively employ context information as an important complementary cue for person matching. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method achieves state-of-the-art results. Also, our model runs at 11.5 fps on a single GPU and can be integrated into the existing end-to-end framework easily.

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