FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning

Abstract

Pedestrian re-identification leverages computer vision technology to achieve cross-camera matching of pedestrians; it has recently led to significant progress and presents numerous practical applications. However, current algorithms face the following challenges: (1) most of the methods are supervised, heavily relying on specific datasets, and lacking robust generalization capabilities; (2) it is hard to extract features because the elongated and narrow shape of pedestrian images introduces uneven feature distributions; (3) the substantial imbalance between positive and negative samples. To address these challenges, we introduce a novel pedestrian re-identification unsupervised algorithm called Feature Fusion Contrastive Learning (FCL) to extract more effective features. Specifically, we employ circular pooling to merge network features across different levels for pedestrian re-identification to improve robust generalization capability. Furthermore, we propose a feature fusion pooling method, which facilitates a more efficient distribution of feature representations across pedestrian images. Finally, we introduce FocalLoss to compute the clustering-level loss, mitigating the imbalance between positive and negative samples. Through extensive experiments conducted on three prominent datasets, our proposed method demonstrates promising performance, with an average 3.8% improvement in FCL’s mAP indicators compared to baseline results.