Online training of object detectors from unlabeled surveillance video

Hasan Çelik; Alan Hanjalic; Emile A. Hendriks; Sabri Boughorbel

doi:10.1109/CVPRW.2008.4563067

Online training of object detectors from unlabeled surveillance video

Hasan Çelik^*, Alan Hanjalic, Emile A. Hendriks, Sabri Boughorbel

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

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.

Original language	English
Title of host publication	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
DOIs	https://doi.org/10.1109/CVPRW.2008.4563067
Publication status	Published - 2008
Externally published	Yes
Event	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops - Anchorage, AK, United States Duration: 23 Jun 2008 → 28 Jun 2008

Publication series

Name	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Conference

Conference	2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
Country/Territory	United States
City	Anchorage, AK
Period	23/06/08 → 28/06/08

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10.1109/CVPRW.2008.4563067

Cite this

Çelik, H., Hanjalic, A., Hendriks, E. A., & Boughorbel, S. (2008). Online training of object detectors from unlabeled surveillance video. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops Article 4563067 (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops). https://doi.org/10.1109/CVPRW.2008.4563067

@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",

booktitle = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops",

note = "2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops ; Conference date: 23-06-2008 Through 28-06-2008",

}

Çelik, H, Hanjalic, A, Hendriks, EA & Boughorbel, S 2008, Online training of object detectors from unlabeled surveillance video. in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops., 4563067, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, Anchorage, AK, United States, 23/06/08. https://doi.org/10.1109/CVPRW.2008.4563067

Online training of object detectors from unlabeled surveillance video. / Çelik, Hasan; Hanjalic, Alan; Hendriks, Emile A. et al.
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. 4563067 (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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T3 - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

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ER -

Çelik H, Hanjalic A, Hendriks EA, Boughorbel S. Online training of object detectors from unlabeled surveillance video. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops. 2008. 4563067. (2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops). doi: 10.1109/CVPRW.2008.4563067

Online training of object detectors from unlabeled surveillance video

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