Adversarial Disentanglement and Correlation Network for Rgb-Infrared Person Re-Identification

Abstract

RGB-infrared person re-identification is a challenging task for intelligent video surveillance. Compared to traditional person re-identification, it concerns the additional modality discrepancy between RGB and infrared images originated from the different imaging processes of spectrum cameras, as well as the pedestrian's appearance discrepancy. In this work, we propose a novel Adversarial Disentanglement and Correlation Network (ADCNet) towards learning modality-invariant and discriminative representations of pedestrians for RGB-infrared person re-identification. ADCNet consists of a feature disentanglement network and a feature alignment network. The feature disentanglement network is designed with an auto-encoder and an adversarial learning module to unify the representations for images across modalities, and the feature alignment network is developed with multiple second-order correlation blocks to employ the second-order non-local position-wise operations for refining the representations and shrinking the intra-modality variations. Extensive experimental results on two challenging benchmarks have demonstrated the effectiveness of the proposed method.