Surveillance scene representation and trajectory abnormality detection using aggregation of multiple concepts

This paper presents a graph-based representation of a given surveillance scene and learning of relevant features including origin, destination, path, speed, size, etc. These features are combined and correlated with target behaviors to detect abnormalities in moving object trajectories. We also propose an aggregation method that reduces the number of missed alarms during aggregation. Several cases using publicly available surveillance video datasets have been presented and the results indicate that the proposed method can be useful to design intelligent and expert surveillance systems.