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.

在智能视频监控中，视频异常检测 (VAD) 涉及检测视频中的异常事件，这是一个复杂而重要的任务。本文提出了一种基于先验知识引导的网络 (PKG-Net) 来解决 VAD 任务。通过在教师 - 学生架构中将自编码器网络与两个指定的代理任务相结合，即未来帧预测和教师网络模仿，提供了更好的未知样本泛化能力。此外，还提出了对适当特征块进行知识蒸馏，以增加模型的多尺度检测能力。实验结果在三个公开基准上验证了我们方法的有效性和准确性，超过了最近的最先进方法。