Attribute disentanglement and registration for occluded person re-identification

Abstract

Occluded person re-identification is a challenging task which suffers from various obstacles. However, existing occluded Re-ID methods tend to exploit body detectors for pedestrian alignment, which are over-reliant on detection and lack of a flexible matching mechanism. To address this issue, we propose an Attribute Disentanglement and Registration (ADR) network to excavate non-occluded regions via attribute feature disentanglement, which can be matched effectively with a robust and soft attribute registration. The proposed ADR takes full advantages of pedestrian attributes’ high-level semantic concepts to alleviate the occlusion problem. First, the Attribute Disentanglement (AD) module obtains the representations of different attributes by localizing their spatial positions. Then the Attribute Registration (AR) module searches and matches these localized regions between different pedestrian images to conduct a registration, which allows the attribute features to be adaptively and efficiently matched. Extensive experiments on occluded, partial, and holistic Re-ID benchmarks demonstrate the effectiveness of the proposed ADR framework as well as its superiority over the existing state-of-the-art methods.