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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

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To link to this item DOI: 10.1016/j.patrec.2013.01.018


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
Divisions: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


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