@inproceedings{990bf3d749eb457fb1226fea69fae6f5,
title = "Online training of object detectors from unlabeled surveillance video",
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.",
author = "Hasan {\c C}elik and Alan Hanjalic and Hendriks, \{Emile A.\} and Sabri Boughorbel",
year = "2008",
doi = "10.1109/CVPRW.2008.4563067",
language = "English",
isbn = "9781424423408",
series = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops",
publisher = "IEEE Computer Society",
booktitle = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops",
address = "United States",
note = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008 ; Conference date: 23-06-2008 Through 28-06-2008",
}