针对矿井视频监视系统中的人员身份难识别的问题，开发了一种基于矿灯光信号的井下作业人员身份视频识别系统。通过在矿灯中嵌入近红外LED光信号，设计了便于识别与追踪的光信号矿灯；设计了基于曼彻斯特编码的光信号编码方法，提出包括霍夫圆/椭圆检测方法在内的矿灯目标检测系列方法，以及动态优化解码方法。视频识别系统通过矿灯发射唯一性ID光信号，视频监视系统采集并处理光信号，解析出矿灯ID。通过煤矿井下实验验证了视频识别系统的有效性。研究成果为矿井安全生产提供了一种创新的人员身份识别技术。

Aiming at the problem of difficult identification of personnel in mine video surveillance system, a video identification system based on light signal of miner's headlamp was developed. By embedding near infrared LED light signal in the headlamp, the light signal was designed to be easy to identify and track.In this study, an optical signal coding method based on Manchester code was designed, and a series of detection methods including Hough circle/ellipse detection method and dynamic optimization decoding method were proposed.The recognition identification system transmits the unique ID optical signal through the miner's headlamp, and the video surveillance system collected and processed the light signal to parse the miner's headlamp ID. The effectiveness of the video recognition system was verified by underground experiment in coal mine. The research results provide an innovative personnel identification technology for mine safety production.