Real-Time Security Risk Assessment From Cctv Using Hand Gesture Recognition

Abstract

Closed-Circuit Television (CCTV) surveillance systems, long associated with physical security, are becoming more crucial when combined with cybersecurity measures. Combining traditional surveillance with cyber defenses is a flexible method for protecting against both physical and digital dangers. This study introduces the use of convolutional neural networks (CNNs) and hand gesture detection using CCTV data to perform real-time security risk assessments. The suggested method's emphasis on automated extraction of key information, such as identity and behavior, illustrates its special use in silent or acoustically challenging settings. This study uses deep learning techniques to develop a novel approach for detecting hand gestures in CCTV images by automatically extracting relevant features using a media-pipe architecture. For instance, it facilitates risk assessment through the use of hand gestures in noisy environments or muted audio streams. Given this method's uniqueness and efficiency, the suggested solution will be able to alert appropriate authorities in the event of a security breach. There seems to be considerable opportunity for the development of applications in several domains of security, law enforcement, and public safety, including but not limited to shopping malls, educational institutions, transportation, the armed forces, theft, abduction, etc.