基于视频的人体行走参数测量方法及应用

针对现有的人体行走参数测量方法复杂度高、效率低等问题，提出了一种基于视频的人体行走参数测量方法。利用监督学习的方法对视频中的运动目标进行姿态估计，逐帧识别骨骼关节点。然后根据头部和脚部特征点，结合场景标定获取的像素距离与实际距离的转换关系，实现行走身高测量；根据关节特征点，利用余弦公式计算关节活动度；根据脚部特征点，提出了一种结合前后极点帧差和像素差判断行走步长和步速的方法。最后提出了一种基于Unity3D的虚拟人随动控制方法，能够在虚拟场景中进行运动仿真，便于实时监控和分析视频中的人体异常行为并做出预警。实验表明该方法具有操作简单、准确度高和实时性强等优点。

Video-based human walking parameter measurement method and application

To address the problems of high complexity and low efficiency of the available methods for human walking parameter measurement, a video-based human walking parameter measurement method was proposed. The supervised learning method was used to estimate the pose of the moving target in the video, and the bone joint points were recognized frame by frame. Then, according to the feature points of head and feet, combined with the conversion relationship between the pixel distance obtained by scene calibration and the actual distance, the walking height measurement was realized. According to the joint feature points, the joint range of motion was calculated by cosine formula. According to the foot feature points, a method to identify the walking step size and pace was proposed by combining the frame difference of front and rear poles and the pixel difference. Finally, a virtual human follow-up control method based on Unity3D was proposed, which could carry out motion simulation in virtual scenes and was convenient for real-time monitoring and human abnormal behavior analysis in videos and early warning issuing. Experiments show that this method is superior in simple operation, high accuracy, and strong real-time performance.