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References

    1. 1)
      • Y. Yang , D. Ramanan .
        1. Yang, Y., Ramanan, D.: ‘Articulated human detection with flexible mixtures of parts’, IEEE Trans PAMI, 2013, 35, (12), pp. 28782890.
        . IEEE Trans PAMI , 12 , 2878 - 2890
    2. 2)
      • P.F. Felzenszwalb , D.P. Huttenlocher .
        2. Felzenszwalb, P.F., Huttenlocher, D.P.: ‘Pictorial structures for object recognition’, IJCV, 2005, 61, (1), pp. 5579.
        . IJCV , 1 , 55 - 79
    3. 3)
      • B. Sapp , C. Jordan , B. Taskar .
        3. Sapp, B., Jordan, C., Taskar, B.: ‘Adaptive pose priors for pictorial structures’. Conf. Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 422429.
        . Conf. Computer Vision and Pattern Recognition , 422 - 429
    4. 4)
      • M. Dantone , J. Gall , C. Leistner .
        4. Dantone, M., Gall, J., Leistner, C., et al: ‘Body parts dependent joint regressors for human pose estimation in still images’, IEEE Trans. PAMI, 2014, 36, (11), pp. 21312143.
        . IEEE Trans. PAMI , 11 , 2131 - 2143
    5. 5)
      • L. Sigal , A. Balan , M.J. Black .
        5. Sigal, L., Balan, A., Black, M.J.: ‘Combined discriminative and generative articulated pose and non-rigid shape estimation’. Neural Information Processing Systems, Vancouver, Canada, 2008, pp. 13371344.
        . Neural Information Processing Systems , 1337 - 1344
    6. 6)
      • D. Zhang , M. Shah .
        6. Zhang, D., Shah, M.: ‘Human pose estimation in videos’. Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 20122020.
        . Int. Conf. Computer Vision , 2012 - 2020
    7. 7)
      • A. Cherian , J. Mairal , K. Alahari .
        7. Cherian, A., Mairal, J., Alahari, K., et al: ‘Mixing body-part sequences for human pose estimation’. Conf. Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 23612368.
        . Conf. Computer Vision and Pattern Recognition , 2361 - 2368
    8. 8)
      • L. Pishchulin , M. Andriluka , P. Gehler .
        8. Pishchulin, L., Andriluka, M., Gehler, P., et al: ‘Poselet conditioned pictorial structures’. Conf. Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 588595.
        . Conf. Computer Vision and Pattern Recognition , 588 - 595
    9. 9)
      • M. Kiefel , P. Gehler .
        9. Kiefel, M., Gehler, P.: ‘Human pose estimation with fields of parts’. European Conf. Computer Vision, Zurich, Switzerland, 2014, pp. 331346.
        . European Conf. Computer Vision , 331 - 346
    10. 10)
      • M. Eichner , V. Ferrari .
        10. Eichner, M., Ferrari, V.: ‘Appearance sharing for collective human pose estimation’. Asian Conf. Computer Vision, Daejeon, Korea, 2013, pp. 138151.
        . Asian Conf. Computer Vision , 138 - 151
    11. 11)
      • C. Wang , Y. Wang , Z. Lin .
        11. Wang, C., Wang, Y., Lin, Z., et al: ‘Robust estimation of 3D human poses from a single image’. Conf. Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 23692376.
        . Conf. Computer Vision and Pattern Recognition , 2369 - 2376
    12. 12)
      • E. Cho , D. Kim .
        12. Cho, E., Kim, D.: ‘Accurate human pose estimation by aggregating multiple pose hypotheses using modified kernel density approximation’, IEEE Signal Process. Lett., 2015, 22, (4), pp. 445449.
        . IEEE Signal Process. Lett. , 4 , 445 - 449
    13. 13)
      • L. Sigal , M. Isard , H. Haussecker .
        13. Sigal, L., Isard, M., Haussecker, H., et al: ‘Loose-limbed people: estimating 3D human pose and motion using non-parametric belief propagation’, IJCV, 2011, 98, (1), pp. 1548.
        . IJCV , 1 , 15 - 48
    14. 14)
      • M. Burenius , J. Sullivan , S. Carlsson .
        14. Burenius, M., Sullivan, J., Carlsson, S.: ‘3D pictorial structures for multiple view articulated pose estimation’. Conf. Computer Vision and Pattern Recognition, Portland, OR, 2013, pp. 36183625.
        . Conf. Computer Vision and Pattern Recognition , 3618 - 3625
    15. 15)
      • A. Schick , R. Stiefelhagen .
        15. Schick, A., Stiefelhagen, R.: ‘3D pictorial structures for human pose estimation with supervoxels’. IEEE Winter Conf. Applications of Computer Vision, Hawaii, Hawaii, 2015, pp. 140147.
        . IEEE Winter Conf. Applications of Computer Vision , 140 - 147
    16. 16)
      • V. Belagiannis , S. Amin , M. Andriluka .
        16. Belagiannis, V., Amin, S., Andriluka, M., et al: ‘3D pictorial structures revisited: multiple human pose estimation’, IEEE T on PAMI, 2015, PP, (99), pp. 11.
        . IEEE T on PAMI , 99 , 1 - 1
    17. 17)
      • C. Canton Ferrer , J.R. Casas , M. Pardas .
        17. Canton Ferrer, C., Casas, J.R., Pardas, M.: ‘Voxel based annealed particle filtering for markerless 3D articulated motion capture’. 3DTV, Potsdam, Germany, 2009, pp. 14.
        . 3DTV , 1 - 4
    18. 18)
      • S. Zuffi , M.J. Black .
        18. Zuffi, S., Black, M.J.: ‘The stitched puppet: a graphical model of 3D human shape and pose’. Conf. Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 35373546.
        . Conf. Computer Vision and Pattern Recognition , 3537 - 3546
    19. 19)
      • M. Hofmann , D.M. Gavrila .
        19. Hofmann, M., Gavrila, D.M.: ‘Multi-view 3D human pose estimation combining single-frame recovery, temporal integration and model adaptation’. Conf. Computer Vision and Pattern Recognition, Miami, FL, 2009, pp. 22142221.
        . Conf. Computer Vision and Pattern Recognition , 2214 - 2221
    20. 20)
      • V. Kazemi , M. Burenius , H. Azizpour .
        20. Kazemi, V., Burenius, M., Azizpour, H., et al: ‘Multi-view body part recognition with random forests’. British Machine Vision Conf., Bristol, UK, 2013.
        . British Machine Vision Conf.
    21. 21)
      • J. Puwein , L. Ballan , R. Ziegler .
        21. Puwein, J., Ballan, L., Ziegler, R., et al: ‘Joint camera pose estimation and 3D human pose estimation in a multi-camera setup’. Asian Conf. Computer Vision, Singapore, 2014, pp. 473487.
        . Asian Conf. Computer Vision , 473 - 487
    22. 22)
      • S. Amin , M. Andriluka , M. Rohrbach .
        22. Amin, S., Andriluka, M., Rohrbach, M., et al: ‘Multi-view pictorial structures for 3D human pose estimation’. British Machine Vision Conf., Bristol, UK, 2013.
        . British Machine Vision Conf.
    23. 23)
      • P.F. Felzenszwalb , D.P. Huttenlocher .
        23. Felzenszwalb, P.F., Huttenlocher, D.P.: ‘Distance transforms of sampled functions.’, Theory Comput., 2012, 8, (1), pp. 415428.
        . Theory Comput. , 1 , 415 - 428
    24. 24)
      • B. Xiaohan.Nie , C. Xiong , S.C. Zhu .
        24. Xiaohan.Nie, B., Xiong, C., Zhu, S.C.: ‘Joint action recognition and pose estimation from video’. Conf. Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 12931301.
        . Conf. Computer Vision and Pattern Recognition , 1293 - 1301
    25. 25)
      • D. Park , D. Ramanan .
        25. Park, D., Ramanan, D.: ‘Articulated pose estimation with tiny synthetic videos’. Conf. Computer Vision and Pattern Recognition Workshop, Boston, MA, 2015, pp. 5866.
        . Conf. Computer Vision and Pattern Recognition Workshop , 58 - 66
    26. 26)
      • A. Agarwal , B. Triggs .
        26. Agarwal, A., Triggs, B.: ‘Recovering 3D human pose from monocular images’, IEEE Trans. PAMI, 2006, 28, (1), pp. 4458.
        . IEEE Trans. PAMI , 1 , 44 - 58
    27. 27)
      • L. Bo , C. Sminchisescu , A. Kanaujia .
        27. Bo, L., Sminchisescu, C., Kanaujia, A., et al: ‘Fast algorithms for large scale conditional 3D prediction’. Conf. Computer Vision and Pattern Recognition, Anchorage, Alaska, 2008, pp. 18.
        . Conf. Computer Vision and Pattern Recognition , 1 - 8
    28. 28)
      • R. Urtasun , T. Darrell .
        28. Urtasun, R., Darrell, T.: ‘Sparse probabilistic regression for activity-independent human pose inference’. Conf. Computer Vision and Pattern Recognition, Anchorage, Alaska, 2008, pp. 18.
        . Conf. Computer Vision and Pattern Recognition , 1 - 8
    29. 29)
      • W. Ouyang , X. Chu , X. Wang .
        29. Ouyang, W., Chu, X., Wang, X.: ‘Multi-source deep learning for human pose estimation’. Conf. Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 23372344.
        . Conf. Computer Vision and Pattern Recognition , 2337 - 2344
    30. 30)
      • X. Fan , K. Zheng , Y. Lin .
        30. Fan, X., Zheng, K., Lin, Y., et al: ‘Combining local appearance and holistic view: dual-source deep neural networks for human pose estimation’. Conf. Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 13471355.
        . Conf. Computer Vision and Pattern Recognition , 1347 - 1355
    31. 31)
      • J.J. Tompson , A. Jain , Y. LeCun .
        31. Tompson, J.J., Jain, A., LeCun, Y., et al: ‘Joint training of a convolutional network and a graphical model for human pose estimation’. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 17991807.
        . Neural Information Processing Systems , 1799 - 1807
    32. 32)
      • A. Toshev , C. Szegedy .
        32. Toshev, A., Szegedy, C.: ‘Deeppose: human pose estimation via deep neural networks’. Conf. Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 16531660.
        . Conf. Computer Vision and Pattern Recognition , 1653 - 1660
    33. 33)
      • X. Chen , A.L. Yuille .
        33. Chen, X., Yuille, A.L.: ‘Articulated pose estimation by a graphical model with image dependent pairwise relations’. Advances in Neural Information Processing Systems 27, Columbus, OH, 2014, pp. 17361744.
        . Advances in Neural Information Processing Systems 27 , 1736 - 1744
    34. 34)
      • J. Carreira , P. Agrawal , K. Fragkiadaki .
        34. Carreira, J., Agrawal, P., Fragkiadaki, K., et al: ‘Human pose estimation with iterative error feedback’. Conf. Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2016, pp. 47334742.
        . Conf. Computer Vision and Pattern Recognition , 4733 - 4742
    35. 35)
      • W. Yang , W. Ouyang , H. Li .
        35. Yang, W., Ouyang, W., Li, H., et al: ‘End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation’. Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 30733082.
        . Conf. Computer Vision and Pattern Recognition , 3073 - 3082
    36. 36)
      • X. Chu , W. Ouyang , H. Li .
        36. Chu, X., Ouyang, W., Li, H., et al: ‘Structured feature learning for pose estimation’. Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 47154723.
        . Conf. Computer Vision and Pattern Recognition , 4715 - 4723
    37. 37)
      • A. Newell , K. Yang , J. Deng .
        37. Newell, A., Yang, K., Deng, J.: ‘Stacked hourglass networks for human pose estimation’. European Conf. Computer Vision, Amsterdam, The Netherlands, 2016, pp. 483499.
        . European Conf. Computer Vision , 483 - 499
    38. 38)
      • P.F. Felzenszwalb , R.B. Girshick , D. McAllester .
        38. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al: ‘Object detection with discriminatively trained part-based models’, IEEE Trans. PAMI, 2010, 32, (9), pp. 16271645.
        . IEEE Trans. PAMI , 9 , 1627 - 1645
    39. 39)
      • K. Simonyan , A. Zisserman . (2014)
        39. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, CoRR, 2014, abs/1409.1556.
        .
    40. 40)
      • L. Sigal , A.O. Balan , M.J. Black .
        40. Sigal, L., Balan, A.O., Black, M.J.: ‘Humaneva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion’, IJCV, 2010, 87, (1–2), pp. 427.
        . IJCV , 4 - 27
    41. 41)
      • N.P. van der Aa , X. Luo , G.J. Giezeman .
        41. van der Aa, N.P., Luo, X., Giezeman, G.J., et al: ‘Umpm benchmark: a multi-person dataset with synchronized video and motion capture data for evaluation of articulated human motion and interaction’. HICV/Int. Conf. Computer Vision Workshops 2011, Barcelona, Spain, 2011, pp. 12641269.
        . HICV/Int. Conf. Computer Vision Workshops 2011 , 1264 - 1269
    42. 42)
      • N. Dalal , B. Triggs .
        42. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. Conf. Computer Vision and Pattern Recognition, San Diego, CA, 2005, vol. 1, pp. 886893.
        . Conf. Computer Vision and Pattern Recognition , 886 - 893
    43. 43)
      • N. Neverova , C. Wolf , G.W. Taylor . (2015)
        43. Neverova, N., Wolf, C., Taylor, G.W., et al: ‘Hand pose estimation through weakly-supervised learning of a rich intermediate representation’ (Pre-print: arxiv:151106728, 2015).
        .
    44. 44)
      • D. Fourure , R. Emonet , E. Fromont .
        44. Fourure, D., Emonet, R., Fromont, E., et al: ‘Multi-task, multi-domain learning: application to semantic segmentation and pose regression’, 2017, 251, pp. 6880.
        . , 68 - 80
    45. 45)
      • N. Srivastava , G. Hinton , A. Krizhevsky .
        45. Srivastava, N., Hinton, G., Krizhevsky, A., et al: ‘Dropout: a simple way to prevent neural networks from overfitting’, J. Mach. Learn. Res., 2014, 15, (1), pp. 19291958.
        . J. Mach. Learn. Res. , 1 , 1929 - 1958
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