PASS-CCTV: Proactive Anomaly surveillance system for CCTV footage analysis in adverse environmental conditions

In recent decades, the growing deployment of Closed-Circuit Television (CCTV) systems for crime prevention and facility security has accelerated the importance of intelligent surveillance technologies. One of the primary challenges in this field includes varying viewpoints and adverse weather conditions that significantly compromise the accuracy of human tracking and anomaly detection. Moreover, conventional surveillance systems often focus only on specific events within limited scenarios, which restricts their applicability. Existing deep learning approaches also face limitations in adaptability to environmental variations, mainly due to the high maintenance costs involved in data collection. To address these challenges, we present a comprehensive surveillance system that utilizes deep learning to enhance human tracking and anomaly detection across diverse environments. Our approach includes the implementation of novel object filtering algorithms that decrease false positive rates and improve tracking precision. Additionally, our system is capable of monitoring multiple types of abnormal events, such as intrusion, loitering, abandonment, and arson. We further introduce a prompt-based recognition mechanism that enables active user participation in identifying abnormal scenes. Extensive evaluations using the Korea Internet & Security Agency CCTV datasets have demonstrated significant performance enhancements by our system, particularly under challenging weather conditions. Moreover, our system achieved competitive accuracy on the ABODA and FireNet datasets, even without additional training. This research establishes a new baseline for practical surveillance solutions that focus on comprehensive monitoring across various abnormal scenarios.