Context-aware Dynamic Contrastive Learning Network and E-Bike Rider Benchmark for Person Search

Abstract

Person search is a challenging computer vision task that aims to simultaneously detect and re-identify individuals from uncropped gallery images. However, most existing approaches are limited by restricted receptive fields, leading to distorted local feature representations under occlusions or complex poses. Additionally, scale variations hinder model generalization in real-world scenarios. To address these limitations, we introduce a novel E-Bike Rider Search (EBRS) dataset, which comprises 27,501 images capturing 963 distinct IDs across 8 camera views at a large urban intersection in a Chinese city. Furthermore, we propose a Context-aware Dynamic Contrastive Learning (CDCL) framework that dynamically adjusts convolutional weights and performs hard sample mining based on contextual cues, thereby improving discriminative capability for both local details and global features. Extensive experiments show our method achieves state-of-the-art performance on CUHK-SYSU and PRW benchmarks, with competitive results on the challenging EBRS dataset, demonstrating its effectiveness.