Partial person re-identification involves matching pedestrian frames where
only a part of a body is visible in corresponding images. This reflects
practical CCTV surveillance scenario, where full person views are often not
available. Missing body parts make the comparison very challenging due to
significant misalignment and varying scale of the views. We propose Partial
Matching Net (PMN) that detects body joints, aligns partial views and
hallucinates the missing parts based on the information present in the frame
and a learned model of a person. The aligned and reconstructed views are then
combined into a joint representation and used for matching images. We evaluate
our approach and compare to other methods on three different datasets,
demonstrating significant improvements.

本文提出了基于部分人员重识别技术的部分匹配网络（PMN），该技术可检测人体关节、对齐局部视图、通过一些信息和具有学习能力的人员模型来逐步恢复缺失的部分，并将对齐和重建后的视图结合起来用于图像匹配，实验证明该方法在三个数据集上有显着的改善。