  Person search aims to search for a target person among multiple images
recorded by multiple surveillance cameras, which faces various challenges from
both pedestrian detection and person re-identification. Besides the large
intra-class variations owing to various illumination conditions, occlusions and
varying poses, background clutters in the detected pedestrian bounding boxes
further deteriorate the extracted features for each person, making them less
discriminative. To tackle these problems, we develop a novel approach which
guides the network with segmentation masks so that discriminative features can
be learned invariant to the background clutters. We demonstrate that joint
optimization of pedestrian detection, person re-identification and pedestrian
segmentation enables to produce more discriminative features for pedestrian,
and consequently leads to better person search performance. Extensive
experiments on benchmark dataset CUHK-SYSU, show that our proposed model
achieves the state-of-the-art performance with 86.3% mAP and 86.5 top-1
accuracy respectively.
