Video anomaly detection (VAD) is an important but challenging task in
computer vision. The main challenge rises due to the rarity of training samples
to model all anomaly cases. Hence, semi-supervised anomaly detection methods
have gotten more attention, since they focus on modeling normals and they
detect anomalies by measuring the deviations from normal patterns. Despite
impressive advances of these methods in modeling normal motion and appearance,
long-term motion modeling has not been effectively explored so far. Inspired by
the abilities of the future frame prediction proxy-task, we introduce the task
of future video prediction from a single frame, as a novel proxy-task for video
anomaly detection. This proxy-task alleviates the challenges of previous
methods in learning longer motion patterns. Moreover, we replace the initial
and future raw frames with their corresponding semantic segmentation map, which
not only makes the method aware of object class but also makes the prediction
task less complex for the model. Extensive experiments on the benchmark
datasets (ShanghaiTech, UCSD-Ped1, and UCSD-Ped2) show the effectiveness of the
method and the superiority of its performance compared to SOTA prediction-based
VAD methods.

通过将初始和未来的原始帧替换为相应的语义分割地图，我们引入了从单帧进行未来视频预测的代理任务，以克服以往方法在学习更长动态模式方面的挑战，并展示了该方法在基准数据集上的有效性和优越性。