Long-term recurrent convolutional network violent Behaviour recognition with attention mechanism

Abstract

Violent behavior recognition is an important direction of behavior recognition research. For traditional violent behavior recognition algorithms, there is too much background information when processing video information, which will cause greater interference in feature extraction, so the recognition accuracy is not high. Improved on the basis of effective recurrent convolutional network, a long-term recurrent convolutional network with attention mechanism is proposed. In the video preprocessing stage, a variety of attention mechanisms are introduced. In the feature extraction stage, the lightweight end-to-side neural network architecture GhostNet and convLSTM are selected to build a long-term recurrent convolutional network. The global average pooling and fully connected layer are used in the classification process. The combined approach realizes the classification of behaviours. The final results show that in the Hockey dataset, the algorithm in this paper has increased by 0.4% compared to LRCN, in the RWF-2000 dataset with more samples, it has increased by 10.5% compared to LRCN, and has increased by 1.75% compared to I3D, indicating that the algorithm in this paper can effectively suppress the background information. Interference, improve the performance of the algorithm.