Enhancing Situational Awareness: Anomaly Detection Using Real-Time Video Across Multiple Domains
Rajan Singh, Amrit Pal, Shruti Mishra, Abishi Chowdhury · IEEE Access · 2025
Though it is challenging, anomaly detection in real-time video data has significant implications in many disciplines like public safety, education, and agriculture. This paper proposes a novel approach to handle the complexity of dynamic real-world anomaly detection scenarios using three state of the art machine learning models: Convolutional Neural Networks (CNN), Region-based Convolutional Neural Network (R-CNN), and You Only Look Once (YOLO). The proposed system is precisely crafted to fast inform authorities of irregularities, so improving situational awareness and security procedures. Using trained models, applying the most effective one for real-time detection, and extensively investigating video data are part of the study method suggested here. The key objectives are to guarantee flexibility to match numerous circumstances and reduce false positives. First findings show the system’s good performance in numerous settings, thereby offering the basis for additional development and study. Especially, following rigorous training and testing, every model displayed varying degrees of accuracy. Emphasizing its effectiveness in focused detection tasks, the CNN model shown a remarkable accuracy of 73% in spotting anomalies inside specific areas of interest. R-CNN, on the other hand, shown greater performance in demanding environments and an accuracy of 80%. Especially appropriate for quick anomaly identification, YOLO, renowned for its speed and accuracy in real-time object detection, shone out with an amazing 91%. This work greatly helps the field of surveillance technologies by providing specific techniques to increase security and safety in useful surroundings. Emphasizing the transformational possibilities of novel anomaly detection systems, this work underlines the importance of constant research and deployment of innovative concepts to boost security and safety measures.