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

Abstract

Use of CCTV is growing rapidly in surveillance applications. Rapid advancement in machine learning and camera hardware has opened-up adequate scopes to build next generation of expert systems aiming at understanding surveillance environments automatically by detection of trajectory abnormality through analyzing object behavior. Such intelligent surveillance systems should be able to learn and combine multiple concepts of abnormality in real-life scenario and classify the events of interest as normal or abnormal. Primary challenges of such systems are to represent and learn patterns in surveillance scenes and combine multiple concepts of abnormalities to activate the alarm system.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.