Contrastive learning based feature optimization for person Re-ID

Abstract. This paper introduces a groundbreaking approach to Person Re-identification (Re-ID), significantly enhancing accuracy and robustness through contrastive learning, especially under challenging real-world conditions like occlusions. The proposed method addresses the limitations of traditional supervised learning, which often demands extensive labeled data, and unsupervised learning, which can be less accurate due to issues such as occlusion. Our solution features a neural network designed to extract both global and local features essential for identifying individuals across different camera views. The network employs global pooling to generate overall identity representation and horizontal pooling to capture detailed local features crucial for distinguishing person identities. Additionally, we present a novel module that integrates a feature extraction encoder with an MLP to refine the comparison of features. To further enhance the model's performance, we utilize a contrastive loss function, an advancement over the standard InfoNCE Loss, which effectively differentiates between positive and negative samples. Experimental results demonstrate a significant improvement in Person Re-ID accuracy and reliability across various environments. By integrating global and local feature representation and leveraging contrastive learning, our model advances the state-of-the-art in Person Re-ID technology. This innovation has broad applications in video surveillance and automated monitoring systems, setting a new standard for accuracy and reliability in the field.