Charles, J, Pfister, T, Magee, D orcid.org/0000-0003-2170-3103 et al. (2 more authors) (2016) Personalizing Human Video Pose Estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 26 Jun - 01 Jul 2016, Las Vegas. IEEE , pp. 3063-3072. ISBN 978-1-4673-8851-1
Abstract
We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person's appearance to improve pose estimation in long videos. We make the following contributions: (i) we show that given a few high-precision pose annotations, e.g. from a generic ConvNet pose estimator, additional annotations can be generated throughout the video using a combination of image-based matching for temporally distant frames, and dense optical flow for temporally local frames, (ii) we develop an occlusion aware self-evaluation model that is able to automatically select the high-quality and reject the erroneous additional annotations, and (iii) we demonstrate that these high-quality annotations can be used to fine-tune a ConvNet pose estimator and thereby personalize it to lock on to key discriminative features of the person's appearance. The outcome is a substantial improvement in the pose estimates for the target video using the personalized ConvNet compared to the original generic ConvNet. Our method outperforms the state of the art (including top ConvNet methods) by a large margin on three standard benchmarks, as well as on a new challenging YouTube video dataset. Furthermore, we show that training from the automatically generated annotations can be used to improve the performance of a generic ConvNet on other benchmarks.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) 2016 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: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/I01229X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jul 2016 15:23 |
Last Modified: | 21 May 2019 09:54 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CVPR.2016.334 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102220 |