Multi-view pose estimation with mixtures of parts and adaptive viewpoint selection
- Author(s): Emre Dogan 1, 2, 3 ; Gonen Eren 3 ; Christian Wolf 1, 2 ; Eric Lombardi 1 ; Atilla Baskurt 1, 2
-
-
View affiliations
-
Affiliations:
1:
Université de Lyon, CNRS , Villeurbanne , France ;
2: INSA-Lyon, LIRIS , UMR CNRS 5205, F-69621 , France ;
3: Department of Computer Engineering , Galatasaray University , 36 Ciragan Cd, Istanbul 34349 , Turkey
-
Affiliations:
1:
Université de Lyon, CNRS , Villeurbanne , France ;
- Source:
Volume 12, Issue 4,
June
2018,
p.
403 – 411
DOI: 10.1049/iet-cvi.2017.0146 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received
10/03/2017,
Accepted
25/11/2017,
Revised
09/10/2017,
Published
01/12/2017

Full text loading...
/deliver/fulltext/iet-cvi/12/4/IET-CVI.2017.0146.html;jsessionid=2l19ss5f1s1gm.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-cvi.2017.0146&mimeType=html&fmt=ahah
Inspec keywords: pose estimation
Other keywords: adaptive viewpoint selection; Utrecht multiperson motion; HumanEva; human pose estimation; multiview pose estimation; geometric information
Subjects: Image recognition; Computer vision and image processing techniques
References
-
-
1)
-
23. Felzenszwalb, P.F., Huttenlocher, D.P.: ‘Distance transforms of sampled functions.’, Theory Comput., 2012, 8, (1), pp. 415–428.
-
-
2)
-
25. Park, D., Ramanan, D.: ‘Articulated pose estimation with tiny synthetic videos’. Conf. Computer Vision and Pattern Recognition Workshop, Boston, MA, 2015, pp. 58–66.
-
-
3)
-
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. 3618–3625.
-
-
4)
-
8. Pishchulin, L., Andriluka, M., Gehler, P., et al: ‘Poselet conditioned pictorial structures’. Conf. Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 588–595.
-
-
5)
-
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. 473–487.
-
-
6)
-
10. Eichner, M., Ferrari, V.: ‘Appearance sharing for collective human pose estimation’. Asian Conf. Computer Vision, Daejeon, Korea, 2013, pp. 138–151.
-
-
7)
-
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. 1337–1344.
-
-
8)
-
37. Newell, A., Yang, K., Deng, J.: ‘Stacked hourglass networks for human pose estimation’. European Conf. Computer Vision, Amsterdam, The Netherlands, 2016, pp. 483–499.
-
-
9)
-
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. 4715–4723.
-
-
10)
-
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. 2361–2368.
-
-
11)
-
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. 3073–3082.
-
-
12)
-
1. Yang, Y., Ramanan, D.: ‘Articulated human detection with flexible mixtures of parts’, IEEE Trans PAMI, 2013, 35, (12), pp. 2878–2890.
-
-
13)
-
39. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, CoRR, 2014, abs/1409.1556.
-
-
14)
-
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. 1929–1958.
-
-
15)
-
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. 3537–3546.
-
-
16)
-
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).
-
-
17)
-
6. Zhang, D., Shah, M.: ‘Human pose estimation in videos’. Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 2012–2020.
-
-
18)
-
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. 1347–1355.
-
-
19)
-
9. Kiefel, M., Gehler, P.: ‘Human pose estimation with fields of parts’. European Conf. Computer Vision, Zurich, Switzerland, 2014, pp. 331–346.
-
-
20)
-
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. 1–8.
-
-
21)
-
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. 2337–2344.
-
-
22)
-
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. 1736–1744.
-
-
23)
-
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. 445–449.
-
-
24)
-
26. Agarwal, A., Triggs, B.: ‘Recovering 3D human pose from monocular images’, IEEE Trans. PAMI, 2006, 28, (1), pp. 44–58.
-
-
25)
-
44. Fourure, D., Emonet, R., Fromont, E., et al: ‘Multi-task, multi-domain learning: application to semantic segmentation and pose regression’, 2017, 251, pp. 68–80.
-
-
26)
-
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. 140–147.
-
-
27)
-
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. 15–48.
-
-
28)
-
20. Kazemi, V., Burenius, M., Azizpour, H., et al: ‘Multi-view body part recognition with random forests’. British Machine Vision Conf., Bristol, UK, 2013.
-
-
29)
-
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. 1799–1807.
-
-
30)
-
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. 4733–4742.
-
-
31)
-
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. 1293–1301.
-
-
32)
-
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. 2131–2143.
-
-
33)
-
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. 886–893.
-
-
34)
-
28. Urtasun, R., Darrell, T.: ‘Sparse probabilistic regression for activity-independent human pose inference’. Conf. Computer Vision and Pattern Recognition, Anchorage, Alaska, 2008, pp. 1–8.
-
-
35)
-
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. 1627–1645.
-
-
36)
-
3. Sapp, B., Jordan, C., Taskar, B.: ‘Adaptive pose priors for pictorial structures’. Conf. Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 422–429.
-
-
37)
-
32. Toshev, A., Szegedy, C.: ‘Deeppose: human pose estimation via deep neural networks’. Conf. Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1653–1660.
-
-
38)
-
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.
-
-
39)
-
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. 1–4.
-
-
40)
-
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. 1–1.
-
-
41)
-
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. 2369–2376.
-
-
42)
-
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. 1264–1269.
-
-
43)
-
2. Felzenszwalb, P.F., Huttenlocher, D.P.: ‘Pictorial structures for object recognition’, IJCV, 2005, 61, (1), pp. 55–79.
-
-
44)
-
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. 4–27.
-
-
45)
-
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. 2214–2221.
-
-
1)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0146

Related content
content/journals/10.1049/iet-cvi.2017.0146
pub_keyword,iet_inspecKeyword,pub_concept
6
6
