Htike, KK and Hogg, DC (2014) Efficient non-iterative domain adaptation of pedestrian detectors to video scenes. In: Proceedings - 22nd International Conference on Pattern Recognition. 2014 22nd International Conference on Pattern Recognition, 24-28 Aug 2014, Stockholm, Sweden. IEEE , 654 - 659. ISBN 9781479952083
Abstract
Pedestrian detection is an essential step in many important applications of Computer Vision. Most detectors require manually annotated ground-truth to train, the collection of which is labor intensive and time-consuming. Generally, this training data is from representative views of pedestrians captured from a variety of scenes. Unsurprisingly, the performance of a detector on a new scene can be improved by tailoring the detector to the specific viewpoint, background and imaging conditions of the scene. Unfortunately, for many applications it is not practical to acquire this scene-specific training data by hand. In this paper, we propose a novel algorithm to automatically adapt and tune a generic pedestrian detector to specific scenes which may possess different data distributions than the original dataset from which the detector was trained. Most state-of-the-art approaches can be inefficient, require manually set number of iterations to converge and some form of human intervention. Our algorithm is a step towards overcoming these problems and although simple to implement, our algorithm exceeds state-of-the-art performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
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: | 07 Oct 2015 13:47 |
Last Modified: | 31 Jan 2018 13:42 |
Published Version: | http://dx.doi.org/10.1109/ICPR.2014.123 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICPR.2014.123 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:84865 |