Spatial-Driven Features based on Image Dependencies for Person Re-identification

Abstract Person re-identification (Re-ID) aims to search for the same pedestrian in different cameras, which is a crucial research direction in pattern recognition. Recent deep learning methods have advanced the development of Re-ID. However, the existing approaches easily result in performance degradation in the case of larger scene data because they do not adequately consider the spatial dependencies of both the inter-image and the intra-image. The paper proposes a novel Spatial-Driven Network (SDN) to learn particularly discriminative features with abundant semantic information from both the inter-image and the intra-image dependencies for person Re-ID. Firstly, we design a global-correlation attention module to capture the inter-image dependencies among a series of different pedestrian images. Secondly, we present a local-correlation attention module to compute the intra-image dependencies from any pair of pixels within each pedestrian image. Furthermore, we propose a specific network integration mechanism, which carefully combines the above two complementary modules to match well the solution of the spatial dependency problem. We implement numerous experiments to assess the proposed SDN on mainstream person Re-ID databases. The results demonstrate that the proposed SDN outperforms most of the state-of-the-art methods in typical key criteria.