EPAN通过双分支网络提升跨视角行人重识别鲁棒性
EPAN: Robust Pedestrian Re-Identification via Enhanced Alignment Network for IoT Surveillance
- 采用双分支结构提取多尺度对齐特征，缓解视角与环境变化影响
- 在Inspection-Personnel数据集上达到90.09% Rank-1和78.82% mAP
- 适合部署于异构摄像头的物联网安防场景
行人重识别（ReID）在物联网智能环境的监控与安全应用中具有关键作用。本文提出增强型行人对齐网络（EPAN），专为应对多样化的物联网监控条件而设计。EPAN采用双分支架构，有效缓解视角与环境变化带来的影响，在不同尺度和视点下提取对齐信息。实验表明，EPAN具备强大的特征提取能力，在Inspection-Personnel数据集上实现90.09%的Rank-1准确率和78.82%的平均精度（mAP）。该结果凸显其在真实物联网场景中的应用潜力，可实现跨多摄像头的高效可靠行人重识别。代码与数据已在GitHub开源。
原文摘要 · Abstract (English)
Person re-identification (ReID) plays a pivotal role in computer vision, particularly in surveillance and security applications within IoT-enabled smart environments. This study introduces the Enhanced Pedestrian Alignment Network (EPAN), tailored for robust ReID across diverse IoT surveillance conditions. EPAN employs a dual-branch architecture to mitigate the impact of perspective and environmental changes, extracting alignment information under varying scales and viewpoints. Here, we demonstrate EPAN's strong feature extraction capabilities, achieving outstanding performance on the Inspection-Personnel dataset with a Rank-1 accuracy of 90.09% and a mean Average Precision (mAP) of 78.82%. This highlights EPAN's potential for real-world IoT applications, enabling effective and reliable person ReID across diverse cameras in surveillance and security systems. The code and data are available at: https://github.com/ggboy2580/EPAN
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。