  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.
