Motion-Aware Transformer For Occluded Person Re-identification

TL;DR

This paper introduces a motion-aware transformer model that leverages motion information from human postures to improve occluded person re-identification, achieving state-of-the-art results in challenging scenarios.

Contribution

The paper proposes a novel self-supervised deep learning framework using a motion-aware transformer to enhance human part segmentation and re-identification under occlusion.

Findings

Achieves state-of-the-art performance on occluded, partial, and holistic datasets.

Effectively distinguishes human body parts despite occlusion and background noise.

Utilizes motion information to refine human part segmentation.

Abstract

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.