Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification

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 the test set and not fully utilize the information from the training data. To alleviate the above problems, we propose a simple but effective method with Parallel Augmentation and Dual Enhancement (PADE) that is robust on both occluded and non-occluded data, and does not require any auxiliary clues. First, we design a parallel augmentation mechanism (PAM) for occluded Re-ID to generate more suitable occluded data to mitigate the negative effects of unbalanced data. Second, we propose the dual enhancement strategy (DES)for global and local features to promote the context information and details. Experimental results on widely used occluded datasets (OccludedDuke, Partial-REID, and Occluded-ReID) and non-occluded datasets (Market-1501 and DukeMTMC-reID) validate the effectiveness of our method. The code will be available soon.