Anomaly Event Retrieval System from TV News and Surveillance Cameras
Mai-Khiem Tran, Minh–Triet Tran · 2023
In an era defined by the proliferation of digital content and a growing reliance on video data, the need for effective anomaly detection systems has never been more pressing. This paper introduces a sophisticated system architecture designed to address the complex challenges associated with acquiring, processing, and presenting anomaly videos. At its core, our architecture prioritizes openness and modularity, allowing for seamless upgrades and customization. This approach ensures adaptability to evolving technology trends and user preferences. We emphasize the crucial aspects of component interfacing and user interaction, highlighting the integration of feedback mechanisms for ongoing system refinement. Additionally, we contribute significantly to the research community by extending an established anomaly event dataset using proven methods and techniques. This extension enhances the dataset’s breadth and depth, providing a valuable resource for training and evaluating anomaly event retrieval systems. Our paper presents a forward-looking system architecture poised to meet the demands of anomaly video detection while also enriching the available resources for anomaly event research. Subsequent sections will delve into architecture components and methodologies, showcasing its potential to revolutionize modern anomaly detection systems.