基于LSTM神经网络的人体动作识别

Human action recognition based on LSTM neural network

人体动作识别为人机合作提供了基础支撑,机器人通过对操作者动作进行识别和理解,可以提高制造系统的柔性和生产效率。针对人体动作识别问题,在三维骨架数据的基础上,对原始三维骨架数据进行平滑去噪处理以符合人体关节点运动的平滑规律;构建了由静态特征和动态特征组成的融合特征用来表征人体动作;引入了关键帧提取模型来提取人体动作序列中的关键帧以减少计算量;建立了以LSTM神经网络为基础的Bi-LSTM神经网络的人体动作分类模型,引入注意力机制以及Dropout进行人体动作分类识别,并对神经网络的主要参数采用正交试验法进行了参数优化;最后利用公开数据集进行动作识别实验。结果表明,该模型算法对人体动作具有较高的识别率。

Human action recognition provides the basic support for human-computer cooperation.Robots can enhance the flexibility and production efficiency of manufacturing system by recognizing and understanding the operator’s action.To resolve the problem of human motion recognition,the original 3D skeleton data was smoothed and denoised to conform to the smooth rule of human joint-point motion based on 3D skeleton data.The fusion feature composed of static and dynamic features was constructed to represent human action.The key frame extraction model was introduced to extract the key frames in human action sequences to reduce the computing load.A Bi-LSTM neural network model based on LSTM neural network was established to classify human actions,and the attention mechanism and Dropout were utilized to classify and recognize human actions,with the main parameters of the neural network optimized by the orthogonal test method.Finally,the open data set was employed for the action recognition experiment.The results show that the proposed model algorithm has a high recognition rate for human actions.