Real Time Surveillance Criminal Detection System

Contemporary law enforcement demands cutting- edge technological solutions to combat evolving criminal activi- ties Real-time surveillance systems employing facial recognition technology have emerged as pivotal tools in law enforcement for swift criminal identification. This research investigates the imple- mentation of advanced facial recognition algorithms-specificallyYOLO, LBPH, and Fisherface-within a live CCTV monitoring framework. The system's core functionality involves analyzing live video feeds, detecting suspect individuals' faces, and cross- referencing them against a database of known offenders. (The algorithms providing the accuracy of 77%, 81%, 78% respectively)Upon identification, the system initiates immediate alerts to nearby authorities, facilitating prompt intervention. A criticalanalysis of the algorithms' technical aspects, including accuracy, speed, and scalability in live video analysis, forms a signif- icant part of this study. Furthermore, ethical considerations surrounding privacy, bias mitigation, and regulatory frameworksgoverning their usage in public spaces are thoroughly examined. This research sheds light on the efficacy of these algorithms in enhancing law enforcement capabilities for proactive criminal detection. It underscores the need for a balanced approach, acknowledging both the potential benefits and ethical implicationsassociated with the integration of facial recognition technologyin real-time surveillance systems.