ANOMALY XPERT: A Deep Learning Approach to Anomaly Detection

Abstract: In response to increasing crime rates, organizations are deploying surveillance systems with CCTV cameras to detect suspicious activities autonomously. This paper proposes an automated system using transfer learning-based CNN models to track and classify activities like 'Shoplifting,' 'Robbery,' or 'Break-In' in real-time CCTV footage. The framework processes raw camera data, detects objects, tracks activities, and classifies them, generating alerts for authorized personnel. Leveraging transfer learning enhances the precision and effectiveness of the CNN model in identifying security threats. Preliminary evaluations demonstrate promising outcomes, yet additional investigation is warranted to address obstacles such as occlusions and lighting variations. Overall, this system offers a proactive security solution for retail environments, ensuring timely detection and intervention against potential security breaches