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Autor:
Ferryman, James, Hogg, David, Sochman, Jan, Behera, Ardhendu, Rodriguez-Serrano, Jose A., Worgan, Simon, Li, Longzhen, Leung, Valerie, Evans, Murray, Cornic, Philippe, Herbin, Stephane, Schlenger, Stefan, Dose, Michael, Computational Vision Group, School of Systems Engineering, University of Reading, RG6 6AY, UK, School of Computing, University of Leeds, LS2 9JT UK, Centre for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Karlovo namesti 13, 121 35 Praha 2, Czech Republic, Xerox Research Centre Europe, 6 Chemin de Maupertuis, 38240 Meylan, France, formerly University of Leeds, Department of Information Processing and Modelling, ONERA, BP 80100, 91123 Palaiseau Cedex, France, MathWorks, Les Montalets, 2 rue de Paris, 92190 Meudon, France, L-1 Identity Solutions, Universitaetsstr.160, 44801 Bochum, Germany
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 un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::bb28d57645e25186c62b9e713fc0b64e
https://centaur.reading.ac.uk/31241/1/PRLETTERS-D-12-00170.pdf
https://centaur.reading.ac.uk/31241/1/PRLETTERS-D-12-00170.pdf