Instantaneous threat detection based on a semantic representation of activities, zones and trajectories
ten Hove, R.J.M.
van den Broek, S.P.
van Huis, J.R.
van Rest, J.H.C.
Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The theft takes place in very different forms, in the midst of many people who pose no threat. The threats range from explicit, e.g., a person attacking the truck driver, to implicit, e.g., somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone, and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The semantic representation encodes the notions of trajectories, zones and activities. The aim of this representation is to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6% of the tracks are correctly interpreted, when considering the temporal context. *
TS - Technical Sciences ; ELSS - Earth, Life and Social Sciences
To reference this document use:
Physics & Electronics ; Resilient Organisations
Defence, Safety and Security
Safety and Security
II - Intelligent Imaging ; NO - Networked Organisations ; DSS - Distributed Sensor Systems
Signal Image and Video Processing