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SmartSurveil : AI-based Intrusion and Suspicious Behaviour Detection in CCTV

Dr. Devidas Thosar, Mayenk Chanore, Priyanka Belkhede, Shardul Pohanerkar

Abstract


This work involves the design of an AI-powered, deep learning-driven surveillance system named SmartSurveil, capable of proactive monitoring, intrusion detection, and suspicious behavior recognition in CCTV video feeds. The system leverages advanced computer vision models such as YOLOv8 for object detection, MediaPipe/Dlib for facial landmark analysis, and SSIM with histogram techniques for tampering detection. Motion-based algorithms are used for loitering and abnormal activity recognition. A configurable Security Mode enables adaptive responses, including alarms, mobile notifications, and silent alerts through a web-based interface. The methodology integrates real-time video feed acquisition, preprocessing, object detection, and intelligent decision-making to minimize human dependency in monitoring tasks. The expected results include reduced security blind spots, timely alerts, enhanced reliability of surveillance systems, and improved overall safety through automated, intelligent CCTV monitoring.


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References


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