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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-7
Hasan Celik , Delft University of Technology, The Netherlands
Alan Hanjalic , Delft University of Technology, The Netherlands
Emile A. Hendriks , Delft University of Technology, The Netherlands
Sabri Boughorbel , Philips Research, Eindhoven, The Netherlands
ABSTRACT
One of the decisive steps in automated surveillance and monitoring is object detection. A standard approach to constructing object detectors consists of annotating large data sets and using them to train a detector. Nevertheless, due to unavoidable constraints of a typical training data set, supervised approaches are inappropriate for building generic systems applicable to a wide diversity of camera setups and scenes. To make a step towards a more generic solution, we propose in this paper a method capable of learning and detecting, in an online and unsupervised setup, the dominant object class in a general scene. The effectiveness of our method is experimentally demonstrated on four representative video sequences.
CITATION
Hasan Celik, Alan Hanjalic, Emile A. Hendriks, Sabri Boughorbel, "Online training of object detectors from unlabeled surveillance video", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-7, doi:10.1109/CVPRW.2008.4563067
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