Human Action Recognition Based on LSTM Neural Network Algorithm

As a special recursive neural network, LSTM can avoid the dependency problem of RNN long-term memory to a certain extent. Although the data flow control mechanism is similar, its internal operational logic varies greatly. This makes the LSTM neural network algorithm more conducive to the in-depth study of human motion recognition. In this case, this paper discusses human motion recognition based on LSTM neural network algorithm. Two human motion recognition models based on LSTM neural network and Bi-LSTM neural network with attention mechanism and Dropout are constructed. First, the LSTM neural network is used to identify and classify human actions. Then, the Bi-LSTM neural network is used and attention mechanism and Dropout are introduced to compensate for the shortcomings of the LSTM neural network in human action recognition. The Bi-LSTM neural network can extract more comprehensive information, attention mechanism is used to increase attention to main information, and Dropout is used to prevent overfitting of the neural network. Then compare the two motion recognition models. Orthogonal experiments were used to optimize several main parameters in the neural network, and a high recognition rate was achieved.