Human gait identification from extremely low‐quality videos: an enhanced classifier ensemble method
Author:
Affiliation:
1. Department of Computer ScienceUniversity of WarwickCoventryCV4 7ALUK
2. Department of Sciences and Information TechnologyUniversity of Sassari07100SassariItaly
Publisher
Institution of Engineering and Technology (IET)
Subject
Computer Vision and Pattern Recognition,Signal Processing,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2013.0062
Reference24 articles.
1. Mori A. Makihara Y. Yagi Y.: ‘Gait recognition using period‐based phase synchronization for low frame‐rate videos’.Proc. Int'l Conf. Pattern Recognition 2010 pp.2194–2197
2. Al‐Huseiny M. Mahmoodi S. Nixon M.: ‘Gait learning‐based regenerative model: a level set approach’.Proc. Int'l Conf. Pattern Recognition 2010 pp.2644–2647
3. Makihara Y. Mori A. Yagi Y.: ‘Temporal super resolution from a single quasi‐periodic image sequence based on phase registration’.Proc. Asian Conf. Computer Vision 2011 pp.107–120
4. Akae N. Makihara Y. Yagi Y.: ‘Gait recognition using periodic temporal super resolution for low frame‐rate videos’.Proc. Int'l Joint Conf. Biometrics 2011 pp.1–7
5. Akae N. Mansur A. Makihara Y. et al. : ‘Video from nearly still: an application to low frame‐rate gait recognition’.Proc. Conf. on Computer Vision and Pattern Recognition 2012 pp.1537–1543
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