We propose an efficient abnormal event detection model based on a lightweight
masked auto-encoder (AE) applied at the video frame level. The novelty of the
proposed model is threefold. First, we introduce an approach to weight tokens
based on motion gradients, thus avoiding learning to reconstruct the static
background scene. Second, we integrate a teacher decoder and a student decoder
into our architecture, leveraging the discrepancy between the outputs given by
the two decoders to improve anomaly detection. Third, we generate synthetic
abnormal events to augment the training videos, and task the masked AE model to
jointly reconstruct the original frames (without anomalies) and the
corresponding pixel-level anomaly maps. Our design leads to an efficient and
effective model, as demonstrated by the extensive experiments carried out on
three benchmarks: Avenue, ShanghaiTech and UCSD Ped2. The empirical results
show that our model achieves an excellent trade-off between speed and accuracy,
obtaining competitive AUC scores, while processing 1670 FPS. Hence, our model
is between 8 and 70 times faster than competing methods. We also conduct an
ablation study to justify our design.

本研究提出了一种基于轻量级掩蔽自编码器的高效异常事件检测模型，它能够利用运动梯度进行权重计算、结合师生解码器来提高检测效果、通过生成合成异常事件来扩充训练数据，同时将原始帧和对应的像素级异常地图一同重新构建，在三个基准测试上表现出优异的速度和精度。