Research on Action Recognition Based on Deep Learning with Long Short-Term Memory Network and Attention Mechanism

Abstract

Action recognition is a basic and challenging task in the field of computer vision. In this paper, a deep learning action recognition method based on attention mechanism is proposed and successfully applied to several public data sets, with outstanding performance. Firstly, the video frames are sampled based on the improved sampling algorithm, and the video data enhancement algorithm is proposed to preprocess the original data, which will reduce the overfitting probability of the recognition model and reduce the white noise of the data. Then, feature selection is carried out through attention-based residual network. Finally, we completed the action classification by LSTM model and softmax. In addition, a series of ablation experiments were designed to verify the validity of the proposed model. The results indicate that compared with the traditional action recognition model, the proposed method can effectively extract key features, reduce the overfitting caused by a small number of samples, reduce the interference of redundant information through the screening of low-information video frames, and complete the action recognition accurately, quickly, and efficiently.