ReadNet:Towards Accurate ReID with Limited and Noisy Samples

Person re-identification (ReID) is an essential cross-camera retrieval task to identify pedestrians. However, the photo number of each pedestrian usually differs drastically, and thus the data limitation and imbalance problem hinders the prediction accuracy greatly. Additionally, in real-world applications, pedestrian images are captured by different surveillance cameras, so the noisy camera related information, such as the lights, perspectives and resolutions, result in inevitable domain gaps for ReID algorithms. These challenges bring difficulties to current deep learning methods with triplet loss for coping with such problems. To address these challenges, this paper proposes ReadNet, an adversarial camera network (ACN) with an angular triplet loss (ATL). In detail, ATL focuses on learning the angular distance among different identities to mitigate the effect of data imbalance, and guarantees a linear decision boundary as well, while ACN takes the camera discriminator as a game opponent of feature extractor to filter camera related information to bridge the multi-camera gaps. ReadNet is designed to be flexible so that either ATL or ACN can be deployed independently or simultaneously. The experiment results on various benchmark datasets have shown that ReadNet can deliver better prediction performance than current state-of-the-art methods.