AI-Powered Anomaly and Threat Detection for Surveillance Footage Analysis

In the domain of video monitoring, achieving effective anomaly and threat detection has remained a persistent challenge, marked by the limitations of traditional systems, which often suffer from high false positive rates and operational inefficiencies. Traditional systems often fall short due to their reliance on manual monitoring and basic automated rules, leading to high false positive rates and inefficiencies. This study proposes a novel approach by utilizing advanced AI technologies, including the Inflated 3D (I3D) model, sophisticated anomaly detection and object detection models. The proposed system is designed to enhance accuracy and agility in identifying unusual activities in surveillance footage. By leveraging these state-of-the-art models, this study aims to reduce the burden on human operators and improve the adaptability of surveillance systems across diverse environments. This study lays the groundwork for incorporating advanced detection strategies, contributing to the creation of safer public and private spaces through enhanced security measures.