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Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 1913-1921

Flowing convnets for human pose estimation in videos

Author keywords

[No Author keywords available]

Indexed keywords

CLADDING (COATING); COMPUTER VISION; NETWORK ARCHITECTURE; OPTICAL FLOWS;

EID: 84973882951     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.222     Document Type: Conference Paper
Times cited : (580)

References (40)
  • 1
    • 77956002528 scopus 로고    scopus 로고
    • Poselets: Body part detectors trained using 3d human pose annotations
    • 1
    • L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In Proc. CVPR, 2009. 1
    • (2009) Proc. CVPR
    • Bourdev, L.1    Malik, J.2
  • 2
    • 80052651120 scopus 로고    scopus 로고
    • Upper body detection and tracking in extended signing sequences
    • 1, 5, 7
    • P. Buehler, M. Everingham, D. P. Huttenlocher, and A. Zisserman. Upper body detection and tracking in extended signing sequences. IJCV, 2011. 1, 5, 7
    • (2011) IJCV
    • Buehler, P.1    Everingham, M.2    Huttenlocher, D.P.3    Zisserman, A.4
  • 3
    • 84907590378 scopus 로고    scopus 로고
    • Automatic and efficient human pose estimation for sign language videos
    • 1, 5, 7
    • J. Charles, T. Pfister, M. Everingham, and A. Zisserman. Automatic and efficient human pose estimation for sign language videos. IJCV, 2013. 1, 5, 7
    • (2013) IJCV
    • Charles, J.1    Pfister, T.2    Everingham, M.3    Zisserman, A.4
  • 5
    • 85044083010 scopus 로고    scopus 로고
    • Articulated pose estimation with image-dependent preference on pairwise relations
    • 1, 8
    • X. Chen and A. Yuille. Articulated pose estimation with image-dependent preference on pairwise relations. In Proc. NIPS, 2014. 1, 8
    • (2014) Proc. NIPS
    • Chen, X.1    Yuille, A.2
  • 6
    • 84911446929 scopus 로고    scopus 로고
    • Mixing body-part sequences for human pose estimation
    • 5, 7
    • A. Cherian, J. Mairal, K. Alahari, and C. Schmid. Mixing body-part sequences for human pose estimation. In Proc. CVPR, 2014. 5, 7
    • (2014) Proc. CVPR
    • Cherian, A.1    Mairal, J.2    Alahari, K.3    Schmid, C.4
  • 8
    • 84863625140 scopus 로고    scopus 로고
    • 2d articulated human pose estimation and retrieval in (almost) unconstrained still images
    • 1
    • M. Eichner, M. Marin-Jimenez, A. Zisserman, and V. Ferrari. 2d articulated human pose estimation and retrieval in (almost) unconstrained still images. IJCV, 2012. 1
    • (2012) IJCV
    • Eichner, M.1    Marin-Jimenez, M.2    Zisserman, A.3    Ferrari, V.4
  • 10
    • 4644354464 scopus 로고    scopus 로고
    • Pictorial structures for object recognition
    • 1
    • P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. IJCV, 2005. 1
    • (2005) IJCV
    • Felzenszwalb, P.1    Huttenlocher, D.2
  • 11
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • 1
    • R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014. 1
    • (2014) Proc. CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 13
    • 84911427286 scopus 로고    scopus 로고
    • Using k-poselets for detecting people and localizing their keypoints
    • 1
    • G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik. Using k-poselets for detecting people and localizing their keypoints. In Proc. CVPR, 2014. 1
    • (2014) Proc. CVPR
    • Gkioxari, G.1    Hariharan, B.2    Girshick, R.3    Malik, J.4
  • 15
    • 85083953281 scopus 로고    scopus 로고
    • Multi-digit number recognition from street view imagery using deep convolutional neural networks
    • 1
    • I. J. Goodfellow, Y. Bulatov, J. Ibarz, S. Arnoud, and V. Shet. Multi-digit number recognition from street view imagery using deep convolutional neural networks. In Proc. ICLR, 2014. 1
    • (2014) Proc. ICLR
    • Goodfellow, I.J.1    Bulatov, Y.2    Ibarz, J.3    Arnoud, S.4    Shet, V.5
  • 17
  • 18
    • 84977621671 scopus 로고    scopus 로고
    • MoDeep: A deep learning framework using motion features for human pose estimation
    • 2, 8
    • A. Jain, J. Tompson, Y. LeCun, and C. Bregler. MoDeep: A deep learning framework using motion features for human pose estimation. Proc. ACCV, 2014. 2, 8
    • (2014) Proc. ACCV
    • Jain, A.1    Tompson, J.2    LeCun, Y.3    Bregler, C.4
  • 21
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • 1, 3
    • A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In Proc. NIPS, 2012. 1, 3
    • (2012) Proc. NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.3
  • 22
    • 34249661090 scopus 로고    scopus 로고
    • Synergistic face detection and pose estimation with energy-based models
    • 1
    • M. Osadchy, Y. LeCun, and M. Miller. Synergistic face detection and pose estimation with energy-based models. JMLR, 2007. 1
    • (2007) JMLR
    • Osadchy, M.1    LeCun, Y.2    Miller, M.3
  • 24
    • 84989343117 scopus 로고    scopus 로고
    • Deep convolutional neural networks for efficient pose estimation in gesture videos
    • 1, 2, 5, 6, 7, 8
    • T. Pfister, K. Simonyan, J. Charles, and A. Zisserman. Deep convolutional neural networks for efficient pose estimation in gesture videos. Proc. ACCV, 2014. 1, 2, 5, 6, 7, 8
    • (2014) Proc. ACCV
    • Pfister, T.1    Simonyan, K.2    Charles, J.3    Zisserman, A.4
  • 26
    • 84887370243 scopus 로고    scopus 로고
    • Modec: Multimodal decomposable models for human pose estimation
    • 5
    • B. Sapp and B. Taskar. Modec: Multimodal decomposable models for human pose estimation. In Proc. CVPR, 2013. 5
    • (2013) Proc. CVPR
    • Sapp, B.1    Taskar, B.2
  • 27
    • 80052890828 scopus 로고    scopus 로고
    • Parsing human motion with stretchable models
    • 1
    • B. Sapp, D. Weiss, and B. Taskar. Parsing human motion with stretchable models. In Proc. CVPR, 2011. 1
    • (2011) Proc. CVPR
    • Sapp, B.1    Weiss, D.2    Taskar, B.3
  • 28
    • 85083951635 scopus 로고    scopus 로고
    • Overfeat: Integrated recognition, localization and detection using convolutional networks
    • 1, 6
    • P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. Proc. ICLR, 2014. 1, 6
    • (2014) Proc. ICLR
    • Sermanet, P.1    Eigen, D.2    Zhang, X.3    Mathieu, M.4    Fergus, R.5    LeCun, Y.6
  • 30
    • 84937862424 scopus 로고    scopus 로고
    • Two-stream convolutional networks for action recognition in videos
    • 1, 2
    • K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. Proc. NIPS, 2014. 1, 2
    • (2014) Proc. NIPS
    • Simonyan, K.1    Zisserman, A.2
  • 31
    • 84866654638 scopus 로고    scopus 로고
    • Conditional regression forests for human pose estimation
    • 1
    • M. Sun, P. Kohli, and J. Shotton. Conditional regression forests for human pose estimation. In Proc. CVPR, 2012. 1
    • (2012) Proc. CVPR
    • Sun, M.1    Kohli, P.2    Shotton, J.3
  • 32
    • 84911198048 scopus 로고    scopus 로고
    • Deepface: Closing the gap to human-level performance in face verification
    • 1
    • Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proc. CVPR, 2014. 1
    • (2014) Proc. CVPR
    • Taigman, Y.1    Yang, M.2    Ranzato, M.3    Wolf, L.4
  • 34
    • 84866688051 scopus 로고    scopus 로고
    • The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation
    • 1
    • J. Taylor, J. Shotton, T. Sharp, and A. Fitzgibbon. The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation. In Proc. CVPR, 2012. 1
    • (2012) Proc. CVPR
    • Taylor, J.1    Shotton, J.2    Sharp, T.3    Fitzgibbon, A.4
  • 36
    • 84930634156 scopus 로고    scopus 로고
    • Joint training of a convolutional network and a graphical model for human pose estimation
    • 1, 3, 8
    • J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. Proc. NIPS, 2014. 1, 3, 8
    • (2014) Proc. NIPS
    • Tompson, J.1    Jain, A.2    LeCun, Y.3    Bregler, C.4
  • 37
    • 84911381180 scopus 로고    scopus 로고
    • DeepPose: Human pose estimation via deep neural networks
    • 1, 2
    • A. Toshev and C. Szegedy. DeepPose: Human pose estimation via deep neural networks. CVPR, 2014. 1, 2
    • (2014) CVPR
    • Toshev, A.1    Szegedy, C.2
  • 38
  • 39
    • 84887598018 scopus 로고    scopus 로고
    • Articulated human detection with flexible mixtures of parts
    • 1, 7
    • Y. Yang and D. Ramanan. Articulated human detection with flexible mixtures of parts. PAMI, 2013. 1, 7
    • (2013) PAMI
    • Yang, Y.1    Ramanan, D.2
  • 40
    • 84921476116 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks
    • 1
    • M. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. Proc. ECCV, 2014. 1
    • (2014) Proc. ECCV
    • Zeiler, M.1    Fergus, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.