Monitoring cameras are extensively utilized in industrial production to monitor equipment running. With advancements in computer vision, device recognition using image features is viable. This paper presents a vision-assisted identification system that implements real-time automatic equipment labeling through image matching in surveillance videos. The system deploys the ORB algorithm to extract image features and the GMS algorithm to remove incorrect matching points. According to the principles of clustering and template locality, a method known as Local Adaptive Clustering (LAC) has been established to enhance label positioning. This method segments matching templates using the cluster center, which improves the efficiency and stability of labels. The experimental results demonstrate that LAC effectively curtails the label drift.

翻译：监控摄像头在工业生产中被广泛用于设备运行监测。随着计算机视觉技术的发展，利用图像特征进行设备识别已成为可行方案。本文提出一种视觉辅助识别系统，通过监控视频中的图像匹配实现实时自动设备标注。该系统采用ORB算法提取图像特征，并运用GMS算法剔除错误匹配点。基于聚类原理与模板局部性，建立了一种称为局部自适应聚类（LAC）的方法来优化标签定位。该方法通过聚类中心对匹配模板进行分割，提升了标签定位的效率与稳定性。实验结果表明，LAC能有效抑制标签漂移现象。