Anomaly detection based on two global grid motion templates

- We propose a scheme that builds every local grid motion template at each location.
- We propose two motion templates including maximum and minimum grid motion template.
- Our method has a very fast speed for training and testing.

In this paper, we propose a novel method to detect abnormal events from videos based on two global grid motion templates (GGMTs) which are able to capture the motion distribution, space and scale information. The GGMTs contain the maximum and minimum grid motion templates which can effectively distinguish the anomalies from the normal motion distribution. One GGMT is composed of several non-overlap local grid motion templates with each one corresponding to a special location. Each local grid motion template is represented by a motion histogram obtained by computing the maximum/minimum motion distribution from the training samples. Experiments on the public datasets show that our method can effectively detect abnormal events in complex scenes.