ENHANCED CCTV ANALYTIC SOLUTION FOR THEFT DETECTION

ARTIFICIAL INTELLIGENCE

This paper presents an enhanced CCTV analytic solution for theft detection in mobile applications. The solution is designed to detect and prevent theft by analyzing video footage captured by mobile devices, using machine learning algorithms like CNN-YOLOV3 to identify suspicious behaviour patterns. The solution includes object detection, motion tracking, and behaviour analysis algorithms, which are integrated into the mobile application to provide real-time monitoring and alerting. The solution is evaluated using a dataset of video footage captured in a retail store, and is shown to be effective in detecting and preventing theft incidents. The study demonstrates the potential of advanced CCTV analytic solutions for enhancing security in mobile applications, and highlights the importance of integrating machine learning algorithms into mobile devices for real-time monitoring and alerting.The enhanced cctv analytic solution for theft detection is an intelligent system that analyzes video footage in real-time to detect and alert security personnel of potential theft incidents. The system utilizes advanced algorithms and machine learning to identify suspicious behavior, such as loitering, unauthorized access, and suspicious object placement, and generates alerts to prevent theft before it occurs. The solution is designed to enhance security in public spaces, retail environments, and other high-risk areas. the goal of the project would be to develop an app that can help prevent theft by detecting suspicious behavior in real-time and alerting users to potential threats. This would involve using machine learning algorithms to analyze video footage and generate alerts when suspicious behavior is detected. The app would be integrated with a network of cameras that can capture footage from different angles and locations, and would allow users to view live footage and playback recorded footage. Keyword : - Theft detection; Footage; Mobile application ;Monitoring etc.

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