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

James Ferryman, David Hogg, Jan Sochman, Ardhendu Behera, Jose Rodriguez-Serrano, Simon Worgan, Longzhen Li, Valerie Leung, Murray Evans, Philippe Cornic, Stephane Herbin, Stefan Schlenger, Michael Dose

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

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).
Original languageEnglish
Pages (from-to)789-798
JournalPattern Recognition Letters
Volume34
Issue number7
DOIs
Publication statusPublished - 4 Feb 2013

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    Ferryman, J., Hogg, D., Sochman, J., Behera, A., Rodriguez-Serrano, J., Worgan, S., Li, L., Leung, V., Evans, M., Cornic, P., Herbin, S., Schlenger, S., & Dose, M. (2013). Robust abandoned object detection integrating wide area visual surveillance and social context. Pattern Recognition Letters, 34(7), 789-798. https://doi.org/10.1016/j.patrec.2013.01.018