Video anomaly detection is a challenging task due to the lack in approaches
for representing samples. The visual representations of most existing
approaches are limited by short-term sequences of observations which cannot
provide enough clues for achieving reasonable detections. In this paper, we
propose to completely represent the motion patterns of objects by learning from
long-term sequences. Firstly, a Stacked State Machine (SSM) model is proposed
to represent the temporal dependencies which are consistent across long-range
observations. Then SSM model functions in predicting future states based on
past ones, the divergence between the predictions with inherent normal patterns
and observed ones determines anomalies which violate normal motion patterns.
Extensive experiments are carried out to evaluate the proposed approach on the
dataset and existing ones. Improvements over state-of-the-art methods can be
observed. Our code is available at
this https URL

通过学习长期序列完整地表示物体的运动模式，提出了一种堆叠状态机 (SSM) 模型，该模型能够预测未来状态并确定违反正常运动模式的异常，提升了现有方法在视频异常检测方面的效果。