Accessibility navigation


Robust abandoned object detection integrating wide area visual surveillance and social context

Ferryman, J., Hogg, D., Sochman, J., Behera, A., Rodriguez-Serrano, J. A., Worgan, S., Li, L., Leung, V., Evans, M., Cornic, P., Herbin, S., Schlenger, S. and Dose, M. (2013) Robust abandoned object detection integrating wide area visual surveillance and social context. Pattern Recognition Letters, 34 (7). pp. 789-798. ISSN 0167-8655

[img]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.

3MB

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: 10.1016/j.patrec.2013.01.018

Abstract/Summary

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:31241
Uncontrolled Keywords:Wide area video surveillance; Behaviour analysis; Abandoned objects
Publisher:Elsevier

Downloads

Downloads per month over past year

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

Page navigation