基于CNN与双向LSTM的行为识别算法

针对传统行为识别依赖手工提取特征,智能化程度不高,识别精度低的问题,提出一种基于3D骨骼数据的卷积神经网络(CNN)与双向长短期记忆网络(Bi-LSTM)的混合模型。使用3D骨骼数据作为网络输入,CNN提取每个时间步的3D输入数据间的空间特征,Bi-LSTM更深层地提取3D数据序列的时间特征。该混合模型自动提取特征完成分类,实现骨骼数据到识别结果的端对端学习。在UTKinect-Action3D标准数据集上,模型的识别率达到97.5%,在自制Kinect数据集上的准确率达到98.6%,实验结果表明,该网络有效提高了分类准确率,具备可用性和有效性。

Action recognition algorithm based on CNN and bidirectional LSTM

Aiming at the problems that traditional action recognition relies on manual feature extraction,which is not intelligent enough and has low recognition accuracy,a hybrid model of convolutional neural network(CNN)and bidirectional long-term and short-term memory network(Bi-LSTM)based on 3D skeleton data was proposed.3D skeleton data were used as the network input.CNN extracted the spatial features between the 3D input data for each time step,and Bi-LSTM extracted the temporal features of 3D data series more deeply.The hybrid model automatically extracted features to complete classification and achieved end-to-end learning from skeleton data to recognition results.On the UTKinect-Action3D standard dataset,the recognition rate of the model is 97.5%,and the accuracy rate of the self-made Kinect dataset is 98.6%.Experimental results show that the network effectively improves the classification accuracy,which has availability and effectiveness.