Intelligent Video Analytics for Abnormal Event Detection

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Surveillance cameras have become a primary tool for monitoring human movement and preventing unwanted or unintended activities. In today's world, security management professionals increasingly rely on video surveillance to combat crime and mitigate incidents that could adversely affect society. The number of surveillance camera installations has dramatically increased in both the private and public sectors, serving to monitor public activities effectively. Video surveillance is considered one of the most efficient methods for ensuring security. Installing a surveillance camera allows the captured footage to be transmitted to security personnel; however, merely having video footage is insufficient for identifying abnormal activities. To effectively analyze this footage, it is essential to integrate an intelligent system.This paper aims to design and implement an Intelligent Video Analytics Model (IVAM), also known as the Human Object Detection (HOD) method, for analyzing and detecting video-based anomalies and abnormal human activities. IVAM can be deployed alongside surveillance cameras in various public locations such as Institutions, airports, hospitals, shopping malls, and railway stations, allowing for the automatic identification of unusual events. The IVAM was experimented with using MATLAB software, and the results were thoroughly verified. The performance of IVAM was assessed by comparing the obtained results with those from existing approaches, demonstrating that IVAM outperforms contemporary methods concerning accurate anomaly detection, lower error rates, and higher classification accuracy.