An MRF-poselets model for detecting highly articulated humans

Duc Thanh Nguyen; Minh Khoi Tran; Sai Kit Yeung

doi:10.1109/ICCV.2015.228

An MRF-poselets model for detecting highly articulated humans

Duc Thanh Nguyen, Minh Khoi Tran, Sai Kit Yeung

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

2 Citations (Scopus)

Abstract

Detecting highly articulated objects such as humans is a challenging problem. This paper proposes a novel part-based model built upon poselets, a notion of parts, and Markov Random Field (MRF) for modelling the human body structure under the variation of human poses and viewpoints. The problem of human detection is then formulated as maximum a posteriori (MAP) estimation in the MRF model. Variational mean field method, a robust statistical inference, is adopted to approximate the MAP estimation. The proposed method was evaluated and compared with existing methods on different test sets including H3D and PASCAL VOC 2007-2009. Experimental results have favourbly shown the robustness of the proposed method in comparison to the state-of-the-art.

Original language	English
Title of host publication	2015 International Conference on Computer Vision, ICCV 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1967-1975
Number of pages	9
ISBN (Electronic)	9781467383912
DOIs	https://doi.org/10.1109/ICCV.2015.228
Publication status	Published - 17 Feb 2015
Externally published	Yes
Event	15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile Duration: 11 Dec 2015 → 18 Dec 2015

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)	1550-5499

Conference

Conference	15th IEEE International Conference on Computer Vision, ICCV 2015
Country/Territory	Chile
City	Santiago
Period	11/12/15 → 18/12/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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10.1109/ICCV.2015.228

Cite this

Nguyen, D. T., Tran, M. K., & Yeung, S. K. (2015). An MRF-poselets model for detecting highly articulated humans. In 2015 International Conference on Computer Vision, ICCV 2015 (pp. 1967-1975). Article 7410585 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2015.228

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title = "An MRF-poselets model for detecting highly articulated humans",

abstract = "Detecting highly articulated objects such as humans is a challenging problem. This paper proposes a novel part-based model built upon poselets, a notion of parts, and Markov Random Field (MRF) for modelling the human body structure under the variation of human poses and viewpoints. The problem of human detection is then formulated as maximum a posteriori (MAP) estimation in the MRF model. Variational mean field method, a robust statistical inference, is adopted to approximate the MAP estimation. The proposed method was evaluated and compared with existing methods on different test sets including H3D and PASCAL VOC 2007-2009. Experimental results have favourbly shown the robustness of the proposed method in comparison to the state-of-the-art.",

author = "Nguyen, \{Duc Thanh\} and Tran, \{Minh Khoi\} and Yeung, \{Sai Kit\}",

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year = "2015",

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doi = "10.1109/ICCV.2015.228",

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Nguyen, DT, Tran, MK & Yeung, SK 2015, An MRF-poselets model for detecting highly articulated humans. in 2015 International Conference on Computer Vision, ICCV 2015., 7410585, Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 International Conference on Computer Vision, ICCV 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1967-1975, 15th IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 11/12/15. https://doi.org/10.1109/ICCV.2015.228

An MRF-poselets model for detecting highly articulated humans. / Nguyen, Duc Thanh; Tran, Minh Khoi; Yeung, Sai Kit.
2015 International Conference on Computer Vision, ICCV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1967-1975 7410585 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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N2 - Detecting highly articulated objects such as humans is a challenging problem. This paper proposes a novel part-based model built upon poselets, a notion of parts, and Markov Random Field (MRF) for modelling the human body structure under the variation of human poses and viewpoints. The problem of human detection is then formulated as maximum a posteriori (MAP) estimation in the MRF model. Variational mean field method, a robust statistical inference, is adopted to approximate the MAP estimation. The proposed method was evaluated and compared with existing methods on different test sets including H3D and PASCAL VOC 2007-2009. Experimental results have favourbly shown the robustness of the proposed method in comparison to the state-of-the-art.

AB - Detecting highly articulated objects such as humans is a challenging problem. This paper proposes a novel part-based model built upon poselets, a notion of parts, and Markov Random Field (MRF) for modelling the human body structure under the variation of human poses and viewpoints. The problem of human detection is then formulated as maximum a posteriori (MAP) estimation in the MRF model. Variational mean field method, a robust statistical inference, is adopted to approximate the MAP estimation. The proposed method was evaluated and compared with existing methods on different test sets including H3D and PASCAL VOC 2007-2009. Experimental results have favourbly shown the robustness of the proposed method in comparison to the state-of-the-art.

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An MRF-poselets model for detecting highly articulated humans

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