TransLowNet: An Online Framework for Video Anomaly Detection, Classification, and Localization

This work presents TransLowNet, an online framework for video anomaly detection, classification, and spatial localization. The system segments incoming video streams into clips processed by an X3D-S feature extractor to obtain spatio-temporal representations, which are analyzed by dedicated modules for anomaly detection and recognition, while a MoG2-based stage estimates the spatial regions of anomalous activity. Evaluated on the UCF-Crime dataset, TransLowNet achieved 80.0% AUC, 54.5% accuracy, and 20.3% mAP@0.5, offering an efficient and interpretable approach for continuous video surveillance.