  Person search in real-world scenarios is a new challenging computer version
task with many meaningful applications. The challenge of this task mainly comes
from: (1) unavailable bounding boxes for pedestrians and the model needs to
search for the person over the whole gallery images; (2) huge variance of
visual appearance of a particular person owing to varying poses, lighting
conditions, and occlusions. To address these two critical issues in modern
person search applications, we propose a novel Individual Aggregation Network
(IAN) that can accurately localize persons by learning to minimize intra-person
feature variations. IAN is built upon the state-of-the-art object detection
framework, i.e., faster R-CNN, so that high-quality region proposals for
pedestrians can be produced in an online manner. In addition, to relieve the
negative effect caused by varying visual appearances of the same individual,
IAN introduces a novel center loss that can increase the intra-class
compactness of feature representations. The engaged center loss encourages
persons with the same identity to have similar feature characteristics.
Extensive experimental results on two benchmarks, i.e., CUHK-SYSU and PRW, well
demonstrate the superiority of the proposed model. In particular, IAN achieves
77.23% mAP and 80.45% top-1 accuracy on CUHK-SYSU, which outperform the
state-of-the-art by 1.7% and 1.85%, respectively.
