Video anomaly detection (VAD) is an essential yet challenge task in signal
processing. Since certain anomalies cannot be detected by analyzing temporal or
spatial information alone, the interaction between two types of information is
considered crucial for VAD. However, current dual-stream architectures either
limit interaction between the two types of information to the bottleneck of
autoencoder or incorporate background pixels irrelevant to anomalies into the
interaction. To this end, we propose a multi-scale spatial-temporal interaction
network (MSTI-Net) for VAD. First, to pay particular attention to objects and
reconcile the significant semantic differences between the two information, we
propose an attention-based spatial-temporal fusion module (ASTM) as a
substitute for the conventional direct fusion. Furthermore, we inject multi
ASTM-based connections between the appearance and motion pathways of a dual
stream network to facilitate spatial-temporal interaction at all possible
scales. Finally, the regular information learned from multiple scales is
recorded in memory to enhance the differentiation between anomalies and normal
events during the testing phase. Solid experimental results on three standard
datasets validate the effectiveness of our approach, which achieve AUCs of
96.8% for UCSD Ped2, 87.6% for CUHK Avenue, and 73.9% for the ShanghaiTech
dataset.

我们提出了一种多尺度时空交互网络（MSTI-Net），采用基于注意力机制的时空融合模块（ASTM）代替直接融合，并注入多个 ASTM 基础的连接，以促进所有可能的空间时间交互，最终将在多个尺度上学习的正常信息记录在内存中，在测试阶段增强异常和正常事件之间的区分。该方法在三个标准数据集上取得了良好的实验结果，UCSD Ped2 的 AUC 值达到了 96.8％，CUHK Avenue 的 AUC 值达到了 87.6％，ShanghaiTech 数据集的 AUC 值达到了 73.9％。