  Recently, occluded person re-identification(Re-ID) remains a challenging task
that people are frequently obscured by other people or obstacles, especially in
a crowd massing situation. In this paper, we propose a self-supervised deep
learning method to improve the location performance for human parts through
occluded person Re-ID. Unlike previous works, we find that motion information
derived from the photos of various human postures can help identify major human
body components. Firstly, a motion-aware transformer encoder-decoder
architecture is designed to obtain keypoints heatmaps and part-segmentation
maps. Secondly, an affine transformation module is utilized to acquire motion
information from the keypoint detection branch. Then the motion information
will support the segmentation branch to achieve refined human part segmentation
maps, and effectively divide the human body into reasonable groups. Finally,
several cases demonstrate the efficiency of the proposed model in
distinguishing different representative parts of the human body, which can
avoid the background and occlusion disturbs. Our method consistently achieves
state-of-the-art results on several popular datasets, including occluded,
partial, and holistic.
