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

提出了一种非常快的视频异常检测模型，通过从多个高精度的目标级教师模型中提取知识来学习检测异常。通过联合应用标准和对抗蒸馏，利用对每个教师的对抗鉴别器来区分目标和生成的异常图，从而提高了学生的保真度。对三个基准数据集（Avenue，ShanghaiTech，UCSD Ped2）进行实验，结果表明，该方法比最快的竞争方法快 7 倍以上，并且比以目标为中心的模型快 28 到 62 倍，同时获得了与最近方法相当的结果，且速度达到了 1480 FPS，是速度和精度之间的最佳权衡。此外，我们进行了全面的剖析研究，以证明我们的架构设计选择的合理性。