Advanced CCTV Surveillance Anomaly Detection, Alert Generation and Crowd Management using Deep Learning Algorithm

In the landscape of modern technological advancements, this research scrutinizes the shortcomings of prevailing Closed-Circuit Television (CCTV) systems and underscores the critical need for an advanced smart CCTV surveillance paradigm. As we navigate the limitations of existing setups across various dimensions, from accuracy to responsiveness and crowd monitoring, the paper advocates for a revolutionary model that integrates anomaly detection, alert generation, and crowd management seamlessly into live CCTV feeds. By identifying the deficiencies in traditional systems, the study propounds a transformative strategy aimed at enhancing real-time surveillance capabilities. Through the incorporation of cutting-edge technologies, such as anomaly detection algorithms and advanced crowd management techniques, the proposed model seeks to redefine the dynamics of CCTV surveillance, ensuring dynamic response mechanisms to anomalies and efficient crowd control. This research not only contributes to the discourse on bolstering security infrastructures but also acts as a catalyst for the evolution of resilient and adaptive surveillance systems. It advocates for the integration of state-of-the-art technologies to harness the full potential of smart CCTV surveillance within existing networks, thereby fostering a safer and more secure environment aligned with the evolving needs of contemporary society.