Camera-specific Informative Data Augmentation Module for Unbalanced Person Re-identification

Person re-identification (Re-ID) aims at retrieving the same person across the non-overlapped camera networks. Recent works have achieved impressive performance due to the rapid development of deep learning techniques. However, most existing methods have ignored the practical unbalanced property in real-world Re-ID scenarios. In fact, the number of pedestrian images in different cameras vary a lot. Some cameras cover thousands of images while others only have a few. As a result, the camera-unbalanced problem will reduce intra-camera diversity, then the model cannot learn camera-invariant features to distinguish pedestrians from "poor" cameras. In this paper, we design a novel camera-specific informative data augmentation module (CIDAM) to alleviate the proposed camera-unbalanced problem. Specifically, we first calculate the camera-specific distribution online, then refine the "poor" camera-specific covariance matrix with similar cameras defined in the prototype-based similarity matrix. Consequently, informative augmented samples are generated by combining original samples with sampled random vectors in feature space. To ensure these augmented samples can better benefit the model training, we further propose a dynamic-threshold-based contrastive loss. Since augmented samples may not be as real as original ones, we calculate a threshold for each original one dynamically and only push hard negative augmented samples away. Moreover, our CIDAM can be compatible with a variety of existing Re-ID methods. Extensive experiments prove the effectiveness of our method.