Real time Violence and Weapon detection and Alert System

Abstract

With the proliferation of surveillance systems and the increasing need for public safety, real-time violence detection has emerged as a crucial research area. This project presents a novel approach to real-time violence detection leveraging the power of deep learning through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (LSTM) and weapon detection using You Only Look Once algorithm. The proposed system not only accurately identifies violent activities and weapons in video streams but also promptly issues alerts for timely intervention. The proposed system aims to address the challenges associated with accurately and swiftly identifying violent activities and weapons in video streams, making it applicable to diverse settings such as public spaces. The CNN component of the model is designed to automatically extract relevant spatial features from video frames, enabling the network to discern patterns associated with violent actions. Simultaneously, the Bi-LSTM network is employed to capture temporal dependencies and dynamic sequences within the video stream, allowing the model to understand the evolution of events over time. The fusion of spatial and temporal information enhances the robustness and accuracy of violence detection, especially in complex scenarios where contextual information is crucial. To further augment the system's capabilities, we use the You Only Look Once (YOLO) for weapon detection. YOLO is a state-of-the-art object detection algorithm that operates in real-time with high accuracy. By incorporating YOLO into our system, we enhance its ability to not only detect violent activities but also identify the presence of weapons in the scene. This additional functionality adds a crucial layer of security and enables authorities to respond more effectively to potential threats. Overall, the combined approach of CNN, Bi-LSTM, and YOLO offers a comprehensive solution for real-time violence detection in video streams, with the added capability of weapon detection. This integrated system contributes to enhancing public safety and security in various settings, including public spaces, by enabling timely intervention and response to potential threats.