  Person search aims to simultaneously localize and identify a query person
from realistic, uncropped images. To achieve this goal, state-of-the-art models
typically add a re-id branch upon two-stage detectors like Faster R-CNN. Owing
to the ROI-Align operation, this pipeline yields promising accuracy as re-id
features are explicitly aligned with the corresponding object regions, but in
the meantime, it introduces high computational overhead due to dense object
anchors. In this work, we present an anchor-free approach to efficiently
tackling this challenging task, by introducing the following dedicated designs.
First, we select an anchor-free detector (i.e., FCOS) as the prototype of our
framework. Due to the lack of dense object anchors, it exhibits significantly
higher efficiency compared with existing person search models. Second, when
directly accommodating this anchor-free detector for person search, there exist
several major challenges in learning robust re-id features, which we summarize
as the misalignment issues in different levels (i.e., scale, region, and task).
To address these issues, we propose an aligned feature aggregation module to
generate more discriminative and robust feature embeddings. Accordingly, we
name our model as Feature-Aligned Person Search Network (AlignPS). Third, by
investigating the advantages of both anchor-based and anchor-free models, we
further augment AlignPS with an ROI-Align head, which significantly improves
the robustness of re-id features while still keeping our model highly
efficient. Extensive experiments conducted on two challenging benchmarks (i.e.,
CUHK-SYSU and PRW) demonstrate that our framework achieves state-of-the-art or
competitive performance, while displaying higher efficiency. All the source
codes, data, and trained models are available at:
https://github.com/daodaofr/alignps.
