Inter-Intra Camera Identity Learning for Person Re-Identification with Training in Single Camera

Traditional person re-identification (re-ID) methods generally rely on inter-camera person images to smooth the domain disparities between cameras. However, collecting and annotating a large number of inter-camera identities is extremely difficult and time-consuming, and this makes it hard to deploy person re-ID systems in new locations. To tackle this challenge, this paper studies the single-camera-training (SCT) setting where every person in the training set only appears in one camera. In this work, we design a novel inter-intra camera identity learning (I2CIL) framework to effectively address the SCT person re-ID. Specifically, (i) we design a Dual-Branch Identity Learning (DBIL) network consisting of inter-camera and intra-camera learning branches to learn person ID discriminative information. The former learns camera-irrelevant feature representations by constraining the distance of inter-camera negative sample pairs closer than the distance of intra-camera negative sample pairs. The latter focuses on pulling the distance of intra-camera positive sample pairs closer and pushing the distance of intra-camera negative sample pairs further, partially alleviating weak ID discrimination caused by the lack of inter-camera annotations. (ii) We design a Mixed-Sampling Joint Learning (MSJL) strategy, which is capable to capture inter- and intra-camera samples and independently accomplish the inter- and intra-camera learning tasks at the same time, avoiding the mutual interference between the two tasks. Extensive experiments on two public SCT datasets prove the superiority of the proposed approach.