Human Violence Detection in Videos Using Key Frame Identification and 3D CNN with Convolutional Block Attention Module

In recent years, there has been an increase in demand for intelligent automatic surveillance systems to detect abnormal activities at various places, such as schools, hospitals, prisons, psychiatric centers, and public gatherings. The availability of video surveillance cameras in such places enables techniques for automatically identifying violent actions and alerting the authorities to minimize loss. Deep learning-based models, such as Convolutional Neural Networks (CNNs), have shown better performance in detecting violent activities by utilizing the spatiotemporal features of video frames. In this work, we propose a violence detection model based on 3D CNN, which employs a DenseNet architecture for enhanced spatiotemporal feature capture. First, the video’s redundant frames are discarded by identifying the key frames in the video. We exploit the Multi-Scale Structural Similarity Index Measure (MS-SSIM) technique to identify the key frames of the video, which contain significant information about the video. Key frame identification helps to reduce the complexity of the model. Next, the identified video key frames with the lowest MS-SSIM are forwarded to 3D CNN to extract spatiotemporal features. Furthermore, we exploit the Convolutional Block Attention Module (CBAM) to increase the representational capabilities of the 3D CNN. The results on different benchmark datasets show that the proposed violence detection method performs better than most of the existing methods. The source code for the proposed method is publicly available at https://github.com/venkateshakula19/violence-detection-using-keyframe-extraction-and-CNN-with-attention-CBAM