LP3DAM: Lightweight Parallel 3D Attention Module for Violence Detection
Jiehang Deng, Yusheng Zheng, Wei Wang, Kunkun Xiong, Kun Zou · 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · 2022
Recent studies have shown that the attention mechanism added to the deep convolutional neural network can effectively improve the network performance, but the attention mechanism applied to the field of violence detection has not been developed. The main reason is that violence detection uses 3D convolution network. At present, most attention modules are only suitable for 2D convolution, and these modules are designed as more complex modules to obtain better network performance, which inevitably increases the complexity of the network model. In order to overcome the trade-off between network performance and complexity, and explore the effectiveness and feasibility of attention mechanism in 3D convolutional network model, this paper proposes Lightweight Parallel 3D Attention Module (LP3DAM), which greatly improves the accuracy of the model by adding a small amount of parameters. Experiments show that LP3DAM has a positive effect on 3D lightweight convolutional networks, which makes the accuracy of the network (MiNet-3D) on the three datasets of Hockey, Crowd and RWF-2000 increase by 1.44%, 4.84% and 0.71%, respectively. The number of parameters added to the original network is controlled within 1K, and the increase of Flops is controlled at about 0.26M.