  Various factors like occlusions, backgrounds, etc., would lead to misaligned
detected bounding boxes , e.g., ones covering only portions of human body. This
issue is common but overlooked by previous person search works. To alleviate
this issue, this paper proposes an Align-to-Part Network (APNet) for person
detection and re-Identification (reID). APNet refines detected bounding boxes
to cover the estimated holistic body regions, from which discriminative part
features can be extracted and aligned. Aligned part features naturally
formulate reID as a partial feature matching procedure, where valid part
features are selected for similarity computation, while part features on
occluded or noisy regions are discarded. This design enhances the robustness of
person search to real-world challenges with marginal computation overhead. This
paper also contributes a Large-Scale dataset for Person Search in the wild
(LSPS), which is by far the largest and the most challenging dataset for person
search. Experiments show that APNet brings considerable performance improvement
on LSPS. Meanwhile, it achieves competitive performance on existing person
search benchmarks like CUHK-SYSU and PRW.
