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We propose a very fast frame-level model for anomaly detection in video,which learns to detect anomalies by distilling knowledge from multiple highlyaccurate object-level teacher models. To improve the fidelity of our student,we distill the low-resolution anomaly maps of the teachers by jointly applyingstandard and adversarial distillation, introducing an adversarial discriminatorfor each teacher to distinguish between target and generated anomaly maps. Weconduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2),showing that our method is over 7 times faster than the fastest competingmethod, and between 28 and 62 times faster than object-centric models, whileobtaining comparable results to recent methods. Our evaluation also indicatesthat our model achieves the best trade-off between speed and accuracy, due toits previously unheard-of speed of 1480 FPS. In addition, we carry out acomprehensive ablation study to justify our architectural design choices.

Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation