CNN and Bilstm Based Framework for Real Life Violence Detection from CCTV Videos

This paper focuses on developing an automated violence detection system for CCTV footage using machine learning techniques. The dataset comprises real-life videos cap-turing instances of violence and non-violent activities in various public spaces. Extensive preprocessing techniques are applied to enhance the quality and diversity of the dataset, including frame extraction, augmentation, and image enhancement. The pro-posed solution adopts a hybrid architecture, integrating a pre- trained MobileNetV2 model with a Bidirectional Long Short- Term Memory (BiLSTM) layer for spatial and temporal feature extraction. Model compilation and training are conducted with appropriate optimization algorithms and callbacks, resulting in a high-performing violence detection system. Performance evaluation metrics demonstrate the efficacy of the developed model, achieving an accuracy rate of 96 % on the test dataset. Comparison with existing techniques underscores the compet-itiveness of the proposed solution.