Human Pose Estimation in Video via MCMC Sampling

Evgeny Shalnov, Anton Konushin

2015

Abstract

We describe a method for the human pose estimation in a video sequence. We propose a new mathematical model of a human pose in a video sequence, which incorporates motion and pose parameters. We show that the model of (Park and Ramanan, 2011) is a particular case of our model. We introduce a framework to infer an approximation of the optimal value in the proposed model. We use an exact algorithm of motion parameters estimation to reduce complexity of inference. Our approach outperforms results of (Park and Ramanan, 2011) in the most complicated video sequences.

References

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Paper Citation


in Harvard Style

Shalnov E. and Konushin A. (2015). Human Pose Estimation in Video via MCMC Sampling . In Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015) ISBN 978-989-758-094-9, pages 71-79. DOI: 10.5220/0005462000710079


in Bibtex Style

@conference{imta-515,
author={Evgeny Shalnov and Anton Konushin},
title={Human Pose Estimation in Video via MCMC Sampling},
booktitle={Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015)},
year={2015},
pages={71-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005462000710079},
isbn={978-989-758-094-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015)
TI - Human Pose Estimation in Video via MCMC Sampling
SN - 978-989-758-094-9
AU - Shalnov E.
AU - Konushin A.
PY - 2015
SP - 71
EP - 79
DO - 10.5220/0005462000710079