  Video anomaly detection (VAD) has been intensively studied for years because
of its potential applications in intelligent video systems. Existing
unsupervised VAD methods tend to learn normality from training sets consisting
of only normal videos and regard instances deviating from such normality as
anomalies. However, they often consider only local or global normality in the
temporal dimension. Some of them focus on learning local spatiotemporal
representations from consecutive frames to enhance the representation for
normal events. But powerful representation allows these methods to represent
some anomalies and causes miss detection. In contrast, the other methods are
devoted to memorizing prototypical normal patterns of whole training videos to
weaken the generalization for anomalies, which also restricts them from
representing diverse normal patterns and causes false alarm. To this end, we
propose a two-branch model, Local-Global Normality Network (LGN-Net), to
simultaneously learn local and global normality. Specifically, one branch
learns the evolution regularities of appearance and motion from consecutive
frames as local normality utilizing a spatiotemporal prediction network, while
the other branch memorizes prototype features of the whole videos as global
normality by a memory module. LGN-Net achieves a balance of representing normal
and abnormal instances by fusing local and global normality. In addition, the
fused normality enables LGN-Net to generalize to various scenes more than
exploiting single normality. Experiments demonstrate the effectiveness and
superior performance of our method. The code is available online:
https://github.com/Myzhao1999/LGN-Net.
