FusedVision: A Knowledge-Infusing Approach for Practical Anomaly Detection in Real-world Surveillance Videos

Object-centric approaches have gained attention as effective one-class classification methods for detecting anomalies in videos. These approaches rely on using an object detector to isolate all objects in the frames and subsequently leveraging either the objects themselves or their interactions to train a learning system. In this study, we put forth a novel perspective towards anomaly detection by proposing a branched network architecture that employs both an object detector and a normalcy learning model, working together in tandem to more effectively identify anomalies within the data. Through extensive experimentation, we analyze the optimal fusion mechanism as well as anomaly scoring proposed in our branched approach. Our approach is more practical towards real-world applications of anomaly detection where infusion of the knowledge about anticipated anomalies may result in better performance while maintaining a baseline performance nonetheless. To evaluate the general applicability of our approach, we integrate it with multiple existing recent anomaly detection methods and assess its efficacy on three widely used anomaly detection datasets: ShanghaiTech, Avenue, and Ped2. Our proposed approach noticeably outperforms existing methods, demonstrating its effectiveness in detecting anomalies across a range of contexts.