Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment

arXiv:2404.18747v1 Announce Type: new
Abstract: Video Anomaly Detection (VAD) identifies unusual activities in video streams, a key technology with broad applications ranging from surveillance to healthcare. Tackling VAD in real-life settings poses significant challenges due to the dynamic nature of human actions, environmental variations, and domain shifts. Many research initiatives neglect these complexities, often concentrating on traditional testing methods that fail to account for performance on unseen datasets, creating a gap between theoretical models and their real-world utility. Online learning …

abstract anomaly anomaly detection applications arxiv challenges cs.ai cs.cv deployment detection dynamic environmental healthcare human inference key life nature online learning surveillance technology type video video streams world