A Hybrid Deep Learning Model for Violence Detection

Current violence detection systems often face challenges such as high computational costs, real-time processing difficulties, and limited precision in detecting violent activities in dynamic environments. This research addresses these issues by proposing a hybrid deep learning model that integrates MobileNetV2 for efficient spatial feature extraction and ConvLSTM (Convolutional Long Short-Term Memory) for capturing temporal patterns. The model effectively discriminates between aggressive and non-aggressive behaviours in real-time video streams. Using the Kaggle Violence Dataset, which comprises 1,000 video clips of violent and non-violent activities, the system achieved an accuracy of 96%, showcasing its high performance. Additionally, an integrated alert system enhances practical application by sending real-time notifications via a Telegram bot, including details such as location, timestamp, and camera ID. This solution ensures timely responses to violent incidents, addressing the core issues of computational efficiency and real-time precision, while offering scalability and applicability in diverse scenarios.