  Video Anomaly Detection (VAD) involves detecting anomalous events in videos,
presenting a significant and intricate task within intelligent video
surveillance. Existing studies often concentrate solely on features acquired
from limited normal data, disregarding the latent prior knowledge present in
extensive natural image datasets. To address this constraint, we propose a
Prior Knowledge Guided Network(PKG-Net) for the VAD task. First, an
auto-encoder network is incorporated into a teacher-student architecture to
learn two designated proxy tasks: future frame prediction and teacher network
imitation, which can provide better generalization ability on unknown samples.
Second, knowledge distillation on proper feature blocks is also proposed to
increase the multi-scale detection ability of the model. In addition,
prediction error and teacher-student feature inconsistency are combined to
evaluate anomaly scores of inference samples more comprehensively. Experimental
results on three public benchmarks validate the effectiveness and accuracy of
our method, which surpasses recent state-of-the-arts.
