Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification

• URL: http://arxiv.org/abs/2210.05438v3
• Date: Wed, 10 Jan 2024 02:03:25 GMT
• Title: Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification
• Authors: Zi Wang, Huaibo Huang, Aihua Zheng, Chenglong Li, Ran He
• Abstract summary: Occluded person re-identification (Re-ID) has attracted lots of attention in the past decades. Recent approaches concentrate on improving performance on occluded data. We propose a simple yet effective method with Parallel Augmentation and Dual Enhancement (PADE) Experimental results on three widely used occluded datasets and two non-occluded datasets validate the effectiveness of our method.
• Score: 70.96277129480478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Occluded person re-identification (Re-ID), the task of searching for the same person's images in occluded environments, has attracted lots of attention in the past decades. Recent approaches concentrate on improving performance on occluded data by data/feature augmentation or using extra models to predict occlusions. However, they ignore the imbalance problem in this task and can not fully utilize the information from the training data. To alleviate these two issues, we propose a simple yet effective method with Parallel Augmentation and Dual Enhancement (PADE), which is robust on both occluded and non-occluded data and does not require any auxiliary clues. First, we design a parallel augmentation mechanism (PAM) to generate more suitable occluded data to mitigate the negative effects of unbalanced data. Second, we propose the global and local dual enhancement strategy (DES) to promote the context information and details. Experimental results on three widely used occluded datasets and two non-occluded datasets validate the effectiveness of our method. The code is available at https://github.com/littleprince1121/PADE_Parallel_Augmentation_and_Dual_Enhancement_for_Occluded_Per son_ReID

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