Detection of Criminal Activities and Anomalies through CCTV’s

Abstract

The detection of criminal activities and anomalies through CCTV (Closed-Circuit Television) surveillance has be- come an essential component of modern security systems. With the rapid advancement of video analytics and machine learning techniques, CCTV systems are now capable of automatically identifying suspicious behavior, unauthorized access, and other criminal activities in real-time. This paper explores the use of AI- based algorithms, including object detection, motion analysis, and facial recognition, to enhance the capabilities of CCTV systems in crime prevention and anomaly detection. By leveraging Advanced deep learning methods, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are utilized to enhance performance and accuracy in various applications, the proposed system can accurately detect abnormal events, track individuals, and flag potential security threats, significantly improving situational awareness. Furthermore, the integration of anomaly detection algorithms can provide proactive alerts for un- usual patterns, enabling quicker responses from law enforcement or security personnel. The study also addresses challenges such as false positives, privacy concerns, and scalability of such systems in large urban environments. Overall, this research highlights the importance of combining intelligent video analysis with traditional surveillance infrastructure to create a more efficient and effective crime detection framework.