Human Interaction Recognition Based on Deep Learning and HMM

This paper proposes a recognition method that combines deep learning with traditional hidden Markov model (HMM) with the aim of improving the recognition accuracy of interaction. First, to construct the classification model, the optimized ALexNet convolutional neural network is used to extract the behavior features, followed by the extraction of features that are used to train the long short-term memory (LSTM) network using the Softmax method. Finally, the particle swarm optimization algorithm is used to fuse the classification results with the traditional HMM classification results so that a hybrid classification model is established to obtain the final behavior recognition result. By conducting experiments on the UT-interaction dataset (six types of interaction behavior), the experimental results show that the hybrid model has higher recognition accuracy than other classical methods.