Human detection and motion recovery based on monocular vision

Abstract

Objective: Provides interactive games and human animation real motion data and technical options. Therefore, how to complete the position, attitude detection, and motion recovery under monocular vision has become an important research direction. Methods: This paper improves the part-based human detection algorithm and uses the AdaBoost multi-instance learning algorithm to train the part detector. Results: The results show that obtaining blood pressure waveform based on monocular vision pulse wave is feasible and has generalization. Conclusions: The results show the feasibility and accuracy of the gait motion detection, motion recovery and analysis system for human lower limbs based on monocular vision. Level of evidence II; Therapeutic studies-investigation of treatment results.