A Lightweight ROI-Based 3D Convolutional Neural Network with Spatial Attention for Violence Detection in Videos

Violence detection in videos is a crucial component of intelligent surveillance systems, enabling early intervention and enhancing public safety in environments such as streets, stations, and stadiums. This study proposes a lightweight ROI-based 3D Convolutional Neural Network (3D CNN) with a spatial attention mechanism for efficient and accurate violence detection in videos. The proposed framework first extracts spatio-temporal clips using dense optical flow to generate regions of interest (ROIs), which are then used to construct 16-frame spatio-temporal clips. These clips are processed by a 3D CNN integrated with spatial attention modules to learn discriminative spatial and temporal features while suppressing background noise. Final classification is performed through fully connected layers with a sigmoid activation function. The proposed model is evaluated on three benchmark datasets, Real-Life Violence (RLV), Hockey Fight, and Action Movies. Experimental results demonstrate strong performance across all datasets. On the Action Movies dataset, the model achieves an accuracy, precision, recall, and F1-score of 98.50%, 97.92%, 97.85%, and 97.88%, respectively. For the Hockey Fight dataset, the corresponding values are 96.10%, 95.40%, 95.20%, and 95.30%, while for the RLV dataset, the model attains 94.85% accuracy, 94.10% precision, 93.90% recall, and 94.00% F1-score. Furthermore, the proposed approach exhibits stable performance across multiple runs, with a standard deviation of less than 1.2%, indicating robustness and consistency. Compared with state-of-the-art models such as ResNet-50, YOLOv9, and baseline 3D CNN architectures incorporating attention mechanisms, the proposed method achieves consistent improvements of approximately 2.5%–6.2% in accuracy across all datasets while maintaining lower computational complexity. The results confirm that the proposed method is both accurate and computationally efficient, making it suitable for real-time violence detection in video surveillance systems.