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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Online training of object detectors from unlabeled surveillance video
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
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
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, pp.1-7, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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