Integration of CNN and LSTM networks for behavior feature recognition: an analysis

This study explores an integration model combining convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) for behavior feature recognition. Initially, a straightforward three-dimensional deep CNN structure was introduced for behavior recognition, capturing static and dynamic characteristics, and analyzing the network's convergence speed. Subsequent experiments utilize the VGG16 CNN model, substituting the fully connected layer with global average pooling. Then, a comparative experiment was conducted on the MSRC-12 behavior dataset between the models. Due to the complexity of LSTM, a simpler GRU model with similar effectiveness was used for comparison. The experimental results showed that the GRU-CNN model performed best, outperforming other algorithms in the literature on the same dataset. Under the same experimental parameters, the GRU-CNN model converges significantly faster than the LSTM-CNN model, with speedier training speed. In addition, the best accuracy is achieved by adjusting the dropout and epoch. Due to cross-validation in this study, the GRU-CNN models achieved good experimental results when the hidden node dropout rate was 0.5. The epoch size had negligible impact on the GRU-CNN model. Still, the accuracy of the CNN and CNN-GRU models increased significantly with more epochs, further validating the effectiveness of the GRU-CNN model. These experiments also indicate that convolutional neural networks based on deep learning are superior to traditional machine learning methods for human behavior recognition. Using depth images instead of conventional images allows for better extraction of spatial features, and the integration with long short-term memory networks enhances the extraction of temporal features from sequences.

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