Person Re-Identification Network Based on Edge-Enhanced Feature Extraction and Inter-Part Relationship Modeling

Abstract

Person re-identification (Re-ID) is a technique for identifying target pedestrians in images or videos. In recent years, owing to the advancements in deep learning, research on person re-identification has made significant progress. However, current methods mostly focus on salient regions within the entire image, overlooking certain hidden features specific to pedestrians themselves. Motivated by this consideration, we propose a novel person re-identification network. Our approach integrates pedestrian edge features into the representation and utilizes edge information to guide global context feature extraction. Additionally, by modeling the internal relationships between different parts of pedestrians, we enhance the network’s ability to capture and understand the interdependencies within pedestrians, thereby improving the semantic coherence of pedestrian features. Ultimately, by fusing these multifaceted features, we generate comprehensive and highly discriminative representations of pedestrians, significantly enhancing person Re-ID performance. Experimental results demonstrate that our method outperforms most state-of-the-art approaches in person re-identification.