基于姿态估计的八段锦序列动作识别与评估

Eight-Section Brocade Sequence Action Recognition and Evaluation Based on Pose Estimation

动作评估与反馈可有效辅助健身运动练习者提高锻炼收益。为了实现八段锦动作的自动量化评估,文中提出一种人体序列动作识别与评估方法。采用姿态估计算法OpenPose提取人体关键点坐标并进行归一化,剔除冗余点。根据动作特点构造出融合关键点位置、距离、关节角度和关键点速度的特征向量,通过多层感知机训练出动作分类器模型。所提方法在KTH和自制八段锦数据集上的动作识别准确率分别达到96.7%和98.7%。基于八段锦动作识别结果构建动作序列,采用动态时间规整算法计算两组八段锦动作序列的相似度,对比实验结果表明该相似度可有效评估动作的完整性及同步性。

Action evaluation and feedback can assist fitness exercisers to improve exercise benefits effectively.In order to realize the automatic quantitative evaluation of eight-section brocade movement,a method of recognition and evaluation of human body sequence movements is proposed.The pose estimation algorithm OpenPose is used to extract the coordinates of the key points of the human body,and then normalize them and eliminate redundant points.According to the characteristics of the action,the feature vector of the fusion key points position,distance,joint angle and key points speed is constructed,and the multi-layer perceptron is employed to train the action classification.The accuracy of action recognition on the KTH and self-made eight-section brocade data sets attains to 96.7%and 98.7%,respectively.Based on the eight-section brocade action recognition results,an action sequence is constructed,and the dynamic time warping algorithm is used to calculate the similarity of the two groups of eight-section brocade action sequences.The comparative experimental results show that the similarity can effectively evaluate the integrity and synchronization of actions.