Proactive Risk Assessment In CCTV Using Hand GestureRecognition And Geolocation Based Alerts

Abstract : CCTV surveillance systems are widely used for security monitoring in various environments, including public spaces, businesses, and homes. However, traditional systems often rely on basic motion detection and manual monitoring, which can miss critical security threats or aggressive behaviors, particularly those indicated by human gestures. Existing methods, such as facial recognition and motion-based alerts, may lack the sensitivity and accuracy needed for effective risk assessment related to hand gestures. To address these limitations, this paper proposes an advanced system that integrates hand gesture recognition into real-time CCTV surveillance. Using OpenPose for gesture recognition and MediaPipe for feature extraction, the system can identify potentially threatening gestures, such as clenched fists or aggressive pointing. Once a high-risk gesture is detected, the system automatically triggers alerts to the nearest police station and authorized personnel through SMS, email, and mobile app notifications. Additionally, the system uses geolocation to ensure that alerts are sent to the appropriate authorities in the vicinity. This proactive approach enables immediate response and improves the effectiveness of surveillance systems, allowing for quicker threat detection and intervention. The integration of hand gesture recognition enhances traditional CCTV systems, providing a more intelligent and responsive security solution that aims to reduce response times and mitigate security risks.

Keyword: CCTV Surveillance, Hand Gesture Recognition, OpenPose, MediaPipe, Security Monitoring, Real-time Detection, Geolocation-based Alerts, Risk Assessment, Threat Mitigation, Smart Surveillance