  Person search aims to simultaneously localize and identify a query person
from realistic, uncropped images, which can be regarded as the unified task of
pedestrian detection and person re-identification (re-id). Most existing works
employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but
with high computational overhead. In this work, we present the Feature-Aligned
Person Search Network (AlignPS), the first anchor-free framework to efficiently
tackle this challenging task. AlignPS explicitly addresses the major
challenges, which we summarize as the misalignment issues in different levels
(i.e., scale, region, and task), when accommodating an anchor-free detector for
this task. More specifically, we propose an aligned feature aggregation module
to generate more discriminative and robust feature embeddings by following a
"re-id first" principle. Such a simple design directly improves the baseline
anchor-free model on CUHK-SYSU by more than 20% in mAP. Moreover, AlignPS
outperforms state-of-the-art two-stage methods, with a higher speed. Code is
available at https://github.com/daodaofr/AlignPS
