Automated Human Violence Detection using MobileNetV2 and Bidirectional LSTM Networks

A cutting-edge automated approach for identifying instances of human aggression in video footage is presented in this research study. By utilizing an advanced combination of MobileNetV2 and Bidirectional Long Short-Term Memory (LSTM) networks, the approach guarantees a strong analysis of consecutive frames. While Bidirectional LSTM records temporal relationships and allows for a more sophisticated understanding of activities over time, MobileNetV2 is a potent feature extractor. After being carefully trained on a wide range of datasets that include both violent and non-violent examples, the model obtains excellent performance metrics. The system has remarkable skill in properly identifying violent acts, with accuracy of 94.5%, precision of 93.0%, recall of 94.5%, and an outstanding F1 score of 95.9%. With its extensive modules for training, testing, data preparation, model design, and visualization, the project offers a solid foundation for critical assessment and real-world application. Subsequent research directions will concentrate on refining the architecture, investigating group learning techniques, and enhancing real-time inference, ultimately advancing automated violence detection in many real-world contexts. There is potential for revolutionary uses of this study in public safety, monitoring, and security.