메뉴 건너뛰기




Volumn 2016-December, Issue , 2016, Pages 4724-4732

Convolutional pose machines

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CONVOLUTION; GESTURE RECOGNITION; PATTERN RECOGNITION;

EID: 85009920674     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.511     Document Type: Conference Paper
Times cited : (3019)

References (44)
  • 1
    • 84911448580 scopus 로고    scopus 로고
    • 2D human pose estimation: New benchmark and state of the art analysis
    • M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele. 2D human pose estimation: New benchmark and state of the art analysis. In CVPR, 2014.
    • (2014) CVPR
    • Andriluka, M.1    Pishchulin, L.2    Gehler, P.3    Schiele, B.4
  • 2
    • 70450203723 scopus 로고    scopus 로고
    • Pictorial structures revisited: People detection and articulated pose estimation
    • M. Andriluka, S. Roth, and B. Schiele. Pictorial structures revisited: People detection and articulated pose estimation. In CVPR, 2009.
    • (2009) CVPR
    • Andriluka, M.1    Roth, S.2    Schiele, B.3
  • 3
    • 77955992058 scopus 로고    scopus 로고
    • Monocular 3D pose estimation and tracking by detection
    • M. Andriluka, S. Roth, and B. Schiele. Monocular 3D pose estimation and tracking by detection. In CVPR, 2010.
    • (2010) CVPR
    • Andriluka, M.1    Roth, S.2    Schiele, B.3
  • 5
    • 77953344311 scopus 로고    scopus 로고
    • PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA
    • D. Bradley. Learning In Modular Systems. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2010.
    • (2010) Learning in Modular Systems
    • Bradley, D.1
  • 7
    • 84937873698 scopus 로고    scopus 로고
    • Articulated pose estimation by a graphical model with image dependent pairwise relations
    • X. Chen and A. Yuille. Articulated pose estimation by a graphical model with image dependent pairwise relations. In NIPS, 2014.
    • (2014) NIPS
    • Chen, X.1    Yuille, A.2
  • 8
    • 84887344431 scopus 로고    scopus 로고
    • Human pose estimation using body parts dependent joint regressors
    • M. Dantone, J. Gall, C. Leistner, and L. Van Gool. Human pose estimation using body parts dependent joint regressors. In CVPR, 2013.
    • (2013) CVPR
    • Dantone, M.1    Gall, J.2    Leistner, C.3    Van Gool, L.4
  • 9
    • 4644354464 scopus 로고    scopus 로고
    • Pictorial structures for object recognition
    • P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. In IJCV, 2005.
    • (2005) IJCV
    • Felzenszwalb, P.1    Huttenlocher, D.2
  • 10
    • 79951563340 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, 2010.
    • (2010) AISTATS
    • Glorot, X.1    Bengio, Y.2
  • 14
    • 84898472539 scopus 로고    scopus 로고
    • Clustered pose and nonlinear appearance models for human pose estimation
    • S. Johnson and M. Everingham. Clustered pose and nonlinear appearance models for human pose estimation. In BMVC, 2010.
    • (2010) BMVC
    • Johnson, S.1    Everingham, M.2
  • 15
    • 80052884516 scopus 로고    scopus 로고
    • Learning effective human pose estimation from inaccurate annotation
    • S. Johnson and M. Everingham. Learning effective human pose estimation from inaccurate annotation. In CVPR, 2011.
    • (2011) CVPR
    • Johnson, S.1    Everingham, M.2
  • 16
    • 84887380431 scopus 로고    scopus 로고
    • Using linking features in learning non-parametric part models
    • L. Karlinsky and S. Ullman. Using linking features in learning non-parametric part models. In ECCV, 2012.
    • (2012) ECCV
    • Karlinsky, L.1    Ullman, S.2
  • 17
    • 84942581361 scopus 로고    scopus 로고
    • Human pose estimation with fields of parts
    • M. Kiefel and P. V. Gehler. Human pose estimation with fields of parts. In ECCV. 2014.
    • (2014) ECCV
    • Kiefel, M.1    Gehler, P.V.2
  • 18
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 19
    • 33745939723 scopus 로고    scopus 로고
    • Beyond trees: Common-factor models for 2D human pose recovery
    • X. Lan and D. Huttenlocher. Beyond trees: Common-factor models for 2D human pose recovery. In ICCV, 2005.
    • (2005) ICCV
    • Lan, X.1    Huttenlocher, D.2
  • 21
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 23
    • 84911409274 scopus 로고    scopus 로고
    • Multi-source deep learning for human pose estimation
    • W. Ouyang, X. Chu, and X. Wang. Multi-source deep learning for human pose estimation. In CVPR, 2014.
    • (2014) CVPR
    • Ouyang, W.1    Chu, X.2    Wang, X.3
  • 24
    • 84973882951 scopus 로고    scopus 로고
    • Flowing convnets for human pose estimation in videos
    • T. Pfister, J. Charles, and A. Zisserman. Flowing convnets for human pose estimation in videos. In ICCV, 2015.
    • (2015) ICCV
    • Pfister, T.1    Charles, J.2    Zisserman, A.3
  • 25
    • 84919790220 scopus 로고    scopus 로고
    • Recurrent convolutional neural networks for scene labeling
    • P. Pinheiro and R. Collobert. Recurrent convolutional neural networks for scene labeling. In ICML, 2014.
    • (2014) ICML
    • Pinheiro, P.1    Collobert, R.2
  • 27
    • 84898795911 scopus 로고    scopus 로고
    • Strong appearance and expressive spatial models for human pose estimation
    • L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele. Strong appearance and expressive spatial models for human pose estimation. In ICCV, 2013.
    • (2013) ICCV
    • Pishchulin, L.1    Andriluka, M.2    Gehler, P.3    Schiele, B.4
  • 29
  • 30
    • 24644504137 scopus 로고    scopus 로고
    • Strike a Pose: Tracking people by finding stylized poses
    • D. Ramanan, D. A. Forsyth, and A. Zisserman. Strike a Pose: Tracking people by finding stylized poses. In CVPR, 2005.
    • (2005) CVPR
    • Ramanan, D.1    Forsyth, D.A.2    Zisserman, A.3
  • 31
    • 80052872903 scopus 로고    scopus 로고
    • Learning message-passing inference machines for structured prediction
    • S. Ross, D. Munoz, M. Hebert, and J. Bagnell. Learning message-passing inference machines for structured prediction. In CVPR, 2011.
    • (2011) CVPR
    • Ross, S.1    Munoz, D.2    Hebert, M.3    Bagnell, J.4
  • 32
    • 84887370243 scopus 로고    scopus 로고
    • MODEC: Multimodal Decomposable Models for Human Pose Estimation
    • B. Sapp and B. Taskar. MODEC: Multimodal Decomposable Models for Human Pose Estimation. In CVPR, 2013.
    • (2013) CVPR
    • Sapp, B.1    Taskar, B.2
  • 33
    • 33845575116 scopus 로고    scopus 로고
    • Measure locally, reason globally: Occlusion-sensitive articulated pose estimation
    • L. Sigal and M. Black. Measure locally, reason globally: Occlusion-sensitive articulated pose estimation. In CVPR, 2006.
    • (2006) CVPR
    • Sigal, L.1    Black, M.2
  • 35
    • 84856628543 scopus 로고    scopus 로고
    • Articulated part-based model for joint object detection and pose estimation
    • M. Sun and S. Savarese. Articulated part-based model for joint object detection and pose estimation. In ICCV, 2011.
    • (2011) ICCV
    • Sun, M.1    Savarese, S.2
  • 37
    • 84887323389 scopus 로고    scopus 로고
    • Exploring the spatial hierarchy of mixture models for human pose estimation
    • Y. Tian, C. L. Zitnick, and S. G. Narasimhan. Exploring the spatial hierarchy of mixture models for human pose estimation. In ECCV. 2012.
    • (2012) ECCV.
    • Tian, Y.1    Zitnick, C.L.2    Narasimhan, S.G.3
  • 39
    • 84930634156 scopus 로고    scopus 로고
    • Joint training of a convolutional network and a graphical model for human pose estimation
    • J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In NIPS, 2014.
    • (2014) NIPS
    • Tompson, J.1    Jain, A.2    LeCun, Y.3    Bregler, C.4
  • 40
    • 84957655992 scopus 로고    scopus 로고
    • DeepPose: Human pose estimation via deep neural networks
    • A. Toshev and C. Szegedy. DeepPose: Human pose estimation via deep neural networks. In CVPR, 2013.
    • (2013) CVPR
    • Toshev, A.1    Szegedy, C.2
  • 41
    • 77956051102 scopus 로고    scopus 로고
    • Auto-context and its application to highlevel vision tasks and 3d brain image segmentation
    • Z. Tu and X. Bai. Auto-context and its application to highlevel vision tasks and 3d brain image segmentation. In TPAMI, 2010.
    • (2010) TPAMI
    • Tu, Z.1    Bai, X.2
  • 42
    • 77951190698 scopus 로고    scopus 로고
    • Multiple tree models for occlusion and spatial constraints in human pose estimation
    • Y. Wang and G. Mori. Multiple tree models for occlusion and spatial constraints in human pose estimation. In ECCV, 2008.
    • (2008) ECCV
    • Wang, Y.1    Mori, G.2
  • 43
    • 80052895150 scopus 로고    scopus 로고
    • Articulated pose estimation with flexible mixtures-of-parts
    • Y. Yang and D. Ramanan. Articulated pose estimation with flexible mixtures-of-parts. In CVPR, 2011.
    • (2011) CVPR
    • Yang, Y.1    Ramanan, D.2
  • 44
    • 84887598018 scopus 로고    scopus 로고
    • Articulated human detection with flexible mixtures of parts
    • Y. Yang and D. Ramanan. Articulated human detection with flexible mixtures of parts. In TPAMI, 2013.
    • (2013) TPAMI
    • Yang, Y.1    Ramanan, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.