Htike, KK and Hogg, D (2014) Unsupervised detector adaptation by joint dataset feature learning. In: Chmielewski, LJ, Kozera, R, Shin, B-S and Wojciechowski, K, (eds.) Computer Vision and Graphics International Conference, ICCVG 2014, Proceedings. International Conference, ICCVG, 15-17 Sep 2014, Warsaw. Springer Verlag , 270 - 277. ISBN 978-3-319-11331-9
Abstract
Object detection is an important step in automated scene understanding. Training state-of-the-art object detectors typically require manual annotation of training data which can be labor-intensive. In this paper, we propose a novel algorithm to automatically adapt a pedestrian detector trained on a generic image dataset to a video in an unsupervised way using joint dataset deep feature learning. Our approach does not require any background subtraction or tracking in the video. Experiments on two challenging video datasets show that our algorithm is effective and outperforms the state-of-the-art approach.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | (c) 2014, Springer. This is an author produced version of a paper published in Computer Vision and Graphics: International Conference, ICCVG 2014, Warsaw, Poland, September 15-17, 2014. Proceedings. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11331-9_33 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Aug 2015 11:35 |
Last Modified: | 21 Feb 2024 14:23 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-11331-9_33 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-319-11331-9_33 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:84866 |