Neighboring-Part Dependency Mining and Feature Fusion Network for Person Re-Identification

Abstract

Person re-identification (Re-ID) is a computer vision technique used to determine the presence of a specific pedestrian target in an image or video sequence. It is an important branch of image retrieval. With the advancements in deep learning, notable progress has been achieved in Re-ID research. However, existing methods primarily focus on the most prominent features in the image, ignoring other less obvious yet beneficial features and spatial interdependencies within the image. To address this issue, this paper proposes a neighboring-part dependency mining and feature fusion network (NDMF-Net). The network horizontally splits pedestrian features into multiple parts, using a part-level hybrid attention module (PHAM) to focus on the salient region of each part, and a neighboring-part dependency exploration module (NDEM) to extract the spatial dependencies between neighboring parts of the image. Eventually, different features are fused to form the final representation. We validate the NDMF-Net on mainstream datasets and the experimental results demonstrate that our method is effective and achieves state-of-the-art performance.

Keywords

• Person re-identification
• deep learning
• part-level hybrid attention
• spatial dependencies
• feature fusion