基于卷积姿态机的潜航员作业姿态识别方法

A new posture recognition and analysis method based on convolutional pose machines was proposed aiming at the problems of complicated recognition process and low recognition accuracy in the existing submariner's operation posture recognition and analysis methods. The human body posture features were structured and coded, and the spatial and projected coordinate system were constructed to explain the human body posture. The calculation formulae of limb angle and the judging processes of special limb state were defined. The spatial and texture features of the RGB operation posture image can be extracted by building the submariner's operation posture recognition algorithm. The joint points, limb angles and state data of the submariner's operation posture can be output. The application of the proposed method was verified by the submariner's operation posture sample data set constructed by collecting submarine operation posture image. The percentage of correct keypoints index value of the recognition algorithm reached 81.2% in the algorithm test. The average accuracy rate of the algorithm in identifying the joint points reached 87.7% in the application verification experiment. The experimental results show that the method is reliable in the recognition and analysis of the submariner's operation posture, and can effectively identify and analyze the negative factors of the submariner's operation posture.