Intelligent Video Surveillance Systems With Violence Detection

Modern video surveillance systems capable of identifying and responding to security threats and violent crimes in real time have become increasingly common in recent years. This study presents a novel approach to enhancing the capabilities of video surveillance systems through the integration of YOLO (You Only Look Once), an advanced object detection algorithm, specifically for human violence detection. The proposed Intelligent Video Surveillance System utilizes YOLOv8’s speed and accuracy to detect violent activity in real-time video feeds. YOLO is particularly well-suited for real-time surveillance applications due to its ability to classify and identify objects in a single network pass. The system leverages a YOLOv8 model that has been fine-tuned on a comprehensive dataset containing both violent and nonviolent activities, allowing it to distinguish between various forms of human violence, including physical altercations, aggressive gestures, and hostile poses. Key components of the system include video feed acquisition, preprocessing, YOLOv8-based object detection, and post-processing techniques to reduce false positives. When compared to traditional surveillance methods, the proposed system offers numerous advantages. By automating the detection process, ensuring continuous monitoring, and responding promptly to potentially harmful situations, it significantly reduces the burden on human operators. Furthermore, the integration of YOLOv8 allows for scalability and flexibility, making the system suitable for diverse surveillance environments, such as public spaces, transportation hubs, and private facilities.