Effective Occlusion Suppression Network via Grouped Pose Estimation for Occluded Person Re-Identification

Zhihao Li, Huaxiang Zhang, Lei Zhu, Jiande Sun, Li Liu

Abstract: The occluded target person often lead to incorrect matching results, so eliminating the interference of background clutter is a key matter. To deal with the challenging occluded person re-identification (Re-ID) tasks, this paper proposes a new grouped pose estimation occlusion generation (GPEOG) network to fully use the person grouped keypoint information and the stable non-occluded features, and make the extracted features more robust and discriminative. The local branch uses the proposed Dynamic Adaptive module (DAM) to automatically adjust the patch size during training process and provides adaptive local features. The occlusion-suppression generation branch mainly includes the proposed effective occlusion suppression (EOS) module. It judges whether the person’s keypoint groups are occluded, and then generates the masks of the corresponding parts to suppress occlusion. Extensive experiments show that the proposed method has obvious advantages compared with several state-of-the-art methods.