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Camera localization for a human-pose in 3D space using a single 2D human-pose image with landmarks: a multimedia social network emerging demand

A Correction to this article was published on 28 November 2018

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Recovering a 3D human-pose in the form of an abstracted skeleton from a 2D image suffers from loss of depth information. Assuming the projected human-pose is represented by a set of 2D landmarks capturing the human-pose limbs, recovering back the original 3D locations is an ill posed problem. To recover a 3D configuration, camera localization in 3D space plays a major role, an inaccurate camera localization might mislead the recovery process. In this paper, we propose a 3D camera localization model using only human-pose appearance in a 2D image (i.e., the set of 2D landmarks). We apply a supervised multi-class logistic regression to assign the camera location in 3D space. In the learning process, we assume a set of predefined labeled camera locations. The features we train consist of relative length of limbs and 2D shape context. The goal is to build a relation between these projected landmarks and the camera location in 3D space. This kind of analysis allows us to reconstruct 3D human-poses based on the 2D projection only without any predefined camera parameters. Also, makes real-time multimedia exchange more reliable specially for human-pose related tasks. We test our model on a set of real images showing a variety of camera locations.

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  • 28 November 2018

    The author regrets that the acknowledgment was left out from the original publication.


  1. 1.

    Akhter I, Black MJ (2015) Pose-conditioned joint angle limits for 3D human pose reconstruction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1446–1455

  2. 2.

    Al-Badarneh A, Khalil M, Al-Hami M (2008) Improving protein 3D structure prediction accuracy using dense regions areas of secondary structures in the contact map. Am J Biochem Biotechnol 4(4):375–384

    Article  Google Scholar 

  3. 3.

    Al-Hami M (2016) Towards a better pose understanding for humanoid robots. PhD thesis, Temple University Libraries

  4. 4.

    Al-Hami M, Khreishah A, Wu J (2013) Video streaming over wireless lan with network coding

  5. 5.

    Al-Hami M, Lakaemper R (2014) Sitting pose generation using genetic algorithm for nao humanoid robots. In: 2014 IEEE workshop on Advanced robotics and its social impacts (ARSO), IEEE, pp 137–142

  6. 6.

    Al-Hami M, Lakaemper R (2015) Towards human pose semantic synthesis in 3D based on query keywords. In: Scitepress

  7. 7.

    Al-Hami M, Lakaemper R (2015) Towards human pose semantic synthesis in 3D based on query keywords. In: VISAPP (3), pp 420–427

  8. 8.

    Al-Hami M, Lakaemper R (2017) Reconstructing 3D human poses from keyword based image database query. In: 2017 International Conference on 3D vision (3DV), IEEE, pp 440–448

  9. 9.

    Awad G, Le DD, Ngo CW, Nguyen VT, Quénot G, Snoek C, Satoh S (2017) Video indexing, search, detection, and description with focus on trecvid. In: Proceedings of the 2017 ACM on international conference on multimedia retrieval, ACM, pp 3–4

  10. 10.

    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522

    Article  Google Scholar 

  11. 11.

    Carreira J, Agrawal P, Fragkiadaki K, Malik J (2016) Human pose estimation with iterative error feedback. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4733–4742

  12. 12.

    Chen CH, Ramanan D (2017) 3D human pose estimation= 2D pose estimation+ matching. In: CVPR. Volume 2, p 6

  13. 13.

    Ferrari V, Marin-Jimenez M, Zisserman A (2008) Progressive search space reduction for human pose estimation. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2008, IEEE, pp 1–8

  14. 14.

    Gavrila D (2000) Pedestrian detection from a moving vehicle. In: Computer Vision ECCV 2000. Springer, pp 37–49

  15. 15.

    Gross R, Shi J (2001) The cmu motion of body (mobo) database

  16. 16.

    Jokinen K, Wilcock G (2014) Multimodal open-domain conversations with the nao robot. In: Natural interaction with Robots, Knowbots and Smartphones. Springer, pp 213–224

  17. 17.

    Lakaemper R KinectTCP documentation. Accessed: 2018-08-8

  18. 18.

    Lan X, Huttenlocher DP (2004) A unified spatio-temporal articulated model for tracking. In: IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR), 2004. Volume 1, IEEE, pp I–722

  19. 19.

    Lan X, Huttenlocher DP (2005) Beyond trees: Common-factor models for 2D human pose recovery. In: Tenth IEEE international Conference on Computer Vision (ICCV), 2005. Volume 1, IEEE, pp 470–477

  20. 20.

    Lin CJ, Weng RC, Keerthi SS (2008) Trust region newton method for logistic regression. J Mach Learn Res 9:627–650

    MathSciNet  MATH  Google Scholar 

  21. 21.

    Mehta D, Sridhar S, Sotnychenko O, Rhodin H, Shafiei M, Seidel HP, Xu W, Casas D, Theobalt C (2017) Vnect: Real-time 3D human pose estimation with a single rgb camera. ACM Transactions on Graphics (TOG) 36(4):44

    Article  Google Scholar 

  22. 22.

    Mousas C, Anagnostopoulos CN (2017) Performance-driven hybrid full-body character control for navigation and interaction in virtual environments. 3D Res 8(2):18

    Article  Google Scholar 

  23. 23.

    Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European conference on computer vision, Springer, pp 483–499

  24. 24.

    Ramakrishna V, Kanade T, Sheikh Y (2012) Reconstructing 3D human pose from 2D image landmarks, pp 573–586

  25. 25.

    Ramanan D (2006) Learning to parse images of articulated bodies. In: Advances in neural information processing systems, pp 1129–1136

  26. 26.

    Rennie JD (2005) Regularized logistic regression is strictly convex. Unpublished manuscript.

  27. 27.

    Sapp B, Taskar B (2013) Modec: Multimodal decomposable models for human pose estimation. In: IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 2013, IEEE, pp 3674–3681

  28. 28.

    Schönemann P (1966) A generalized solution of the orthogonal procrustes problem. Psychometrika 31(1):1–10

    MathSciNet  Article  Google Scholar 

  29. 29.

    Sharma D, Lakhmi J, Favorskaya M, Howlett RJ (2015) Fusion of smart, multimedia and computer gaming technologies. Volume 1. Springer, Berlin

    Google Scholar 

  30. 30.

    Taylor CJ (2000) Reconstruction of articulated objects from point correspondences in a single uncalibrated image. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2000. Volume 1, IEEE, pp 677–684

  31. 31.

    The vicon skeleton template. Accessed: 2016-1-15

  32. 32.

    Varadarajan J, Subramanian R, Bulò SR, Ahuja N, Lanz O, Ricci E (2018) Joint estimation of human pose and conversational groups from social scenes. Int J Comput Vis 126(2-4):410–429

    MathSciNet  Article  Google Scholar 

  33. 33.

    Wang C, Wang Y, Lin Z, Yuille AL, Gao W (2014) Robust estimation of 3D human poses from a single image. In: 2014 IEEE conference on Computer vision and pattern recognition (CVPR), IEEE, pp 2369–2376

  34. 34.

    Yang W, Ouyang W, Li H, Wang X (2016) End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation, pp 3073–3082

  35. 35.

    Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimedia 19 (2):4–10

    Article  Google Scholar 

  36. 36.

    Zhou X, Zhu M, Leonardos S, Derpanis KG, Daniilidis K (2016) Sparseness meets deepness: 3D human pose estimation from monocular video. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4966–4975

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Correspondence to M. Shamim Hossain.

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Al-Hami, M., Lakaemper, R., Rawashdeh, M. et al. Camera localization for a human-pose in 3D space using a single 2D human-pose image with landmarks: a multimedia social network emerging demand. Multimed Tools Appl 78, 3587–3608 (2019).

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  • Human-pose
  • Projection
  • Camera localization
  • Multimedia
  • Logistic regression
  • 2D shape context
  • 3D reconstruction
  • Rotation matrix
  • Translation
  • Extrinsic camera
  • Intrinsic camera
  • Principal component analysis
  • Features
  • Projection error