Semantic Constraint GAN for Person Re-Identification in Camera Sensor Networks

In this paper, we propose a novel data augmentation method named Semantic Constraint Generative Adversarial Network (SCGAN) for person re-identification (Re-ID) in camera sensor networks. The proposed SCGAN can generate multiple style pedestrian images with high-level semantic information. To this end, we design two types of semantic constraints, i.e., attention constraint and identity constraint. The attention constraint aims to restrict the significant areas in the attention map to be consistent before and after image transformation. The identity constraint focuses on keeping the identity of the generated pedestrian image to be the same as that of the real one. After generating pedestrian images using SCGAN, we combine them with the real pedestrian images to train the person Re-ID model. Since the proposed SCGAN increases the diversity of training samples, the generalization of Re-ID model is enhanced. We evaluate the proposed SCGAN on three large-scale person Re-ID databases, i.e., Market1501, CUHK03 and DukeMTMC-reID, and experimental results reveal that the proposed SCGAN yields consistent improvements over other methods.