Occluded Person Re-ID Based on Dual Attention Mask Guidance

Abstract

Occluded person re-identification aims to match occluded pedestrian with pedestrian images from nonintersecting cameras. For dealing with the task of Occluded Person Re-ID, existing methods focus only on tackling the pedestrian occlusion issue. Yet they all ignore the fact that salient nonhuman body parts such as hats, bags, and jewelry can provide more discriminatory informational cues. In this regard, this paper proposed an Occluded Person Re-ID algorithm based on Dual Attention Mask (DAM) guidance, which simultaneously solves the problems of pedestrian occlusion and significant non-human body part features that are easily ignored. Specifically, DAM consists of Pose Attention (PA) and Saliency Attention (SA). PA utilizes human pose key points to generate heatmap, and the produced heatmap indicates whether a specific body part is occluded or not, as well as guides the model to focus on the non-occluded region. SA then locates dropped salient non-human body part features through spatial attention and channel attention, and generates a saliency attention heat map to direct the model focus on the salient non-human body part features. Accordingly, DAM not only highlights the visible body parts while suppressing the occluded parts, but also localizes the prominent non-human body parts in the background. Finally, extensive experiments on three occlusion datasets demonstrate the effectiveness of our proposed method.