Accessibility navigation


Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Burghouts, G. J., Schutte, K., ten Hove, R. J.-M., van den Broek, S. P., Baan, J., Rajadell, O., van Huis, J. R., van Rest, J., Hanckmann, P., Bouma, H., Sanroma, G., Evans, M. and Ferryman, J. (2014) Instantaneous threat detection based on a semantic representation of activities, zones and trajectories. Signal, Image and Video Processing, 8 (S1). pp. 191-200. ISSN 1863-1703

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1007/s11760-014-0672-1

Abstract/Summary

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 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 aim of this representation was 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:39811
Uncontrolled Keywords:Threat detection, Human action recognition, Spatiotemporal features, Tracking of humans, Trajectories Zones
Publisher:Springer London

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation