基于卷积姿态机的潜航员作业姿态识别方法

针对现有潜航员作业姿态识别分析方法中识别过程繁琐、识别精度低的问题，提出基于卷积姿态机的潜航员作业姿态识别分析方法. 对人体姿态特征进行结构化编码，构建空间及投影坐标系进行解析，定义肢体角度计算公式与肢体特殊状态判断流程. 通过搭建潜航员作业姿态识别算法，实现作业姿态RGB图像空间特征与纹理特征的提取，输出潜航员作业姿态关节点、肢体角度与状态数据. 通过采集潜航员作业姿态图像构建潜航员作业姿态样本数据集，对所提方法进行应用验证. 在算法测试中，识别算法的PCK指标值达到81.2%. 在应用验证实验中，算法识别关节点的平均准确率达到87.7%. 该方法在潜航员作业姿态识别分析上是可靠的，可以有效地识别与分析潜航员作业姿态中的危险因素.

Recognition method of submarine operation posture based on convolutional pose machine

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.