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.