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A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera

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Consumer Depth Cameras for Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The 3D reconstruction of complex human motions from 2D color images is a challenging and sometimes intractable problem. The pose estimation problem becomes more feasible when using streams of 2.5D monocular depth images as provided by a depth camera. However, due to low resolution of and challenging noise characteristics in depth camera images as well as self-occlusions in the movements, the pose estimation task is still far from being simple. Furthermore, in real-time scenarios, the reconstruction task becomes even more challenging since global optimization strategies are prohibitive. To facilitate tracking of full-body human motions from a single depth-image stream, we introduce a data-driven hybrid strategy that combines local pose optimization with global retrieval techniques. Here, the final pose estimate at each frame is determined from the tracked and retrieved pose hypotheses which are fused using a fast selection scheme. Our algorithm reconstructs complex full-body poses in real time and effectively prevents temporal drifting, thus making it suitable for various real-time interaction scenarios.

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References

  1. Azad, P., Asfour, T., Dillmann, R.: Robust real-time stereo-based markerless human motion capture. In: IEEE/RAS International Conference on Humanoid Robots, pp. 700–707 (2008)

    Google Scholar 

  2. Baak, A., Müller, M., Bharaj, G., Seidel, H.P., Theobalt, C.: Accompanied video to [3]. http://www.youtube.com/watch?v=QWNn01FWUkk (2011)

  3. Baak, A., Müller, M., Bharaj, G., Seidel, H.P., Theobalt, C.: A data-driven approach for real-time full body pose reconstruction from a depth camera. In: IEEE International Conference on Computer Vision, pp. 1092–1099 (2011)

    Google Scholar 

  4. Baak, A., Rosenhahn, B., Müller, M., Seidel, H.P.: Stabilizing motion tracking using retrieved motion priors. In: IEEE International Conference on Computer Vision, pp. 1428–1435 (2009)

    Chapter  Google Scholar 

  5. Bălan, A.O., Sigal, L., Black, M.J., Davis, J.E., Haussecker, H.W.: Detailed human shape and pose from images. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  6. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  7. Bleiweiss, A., Kutliroff, E., Eilat, G.: Markerless motion capture using a single depth sensor. In: SIGGRAPH ASIA Sketches (2009)

    Google Scholar 

  8. Bo, L., Sminchisescu, C.: Twin gaussian processes for structured prediction. Int. J. Comput. Vis. 87(1–2), 28–52 (2010)

    Article  Google Scholar 

  9. Bregler, C., Malik, J., Pullen, K.: Twist based acquisition and tracking of animal and human kinematics. Int. J. Comput. Vis. 56(3), 179–194 (2004)

    Article  Google Scholar 

  10. Brubaker, M.A., Fleet, D.J., Hertzmann, A.: Physics-based person tracking using the anthropomorphic walker. Int. J. Comput. Vis. 87, 140–155 (2010)

    Article  Google Scholar 

  11. Cormen, T.H., Stein, C., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  12. Demirdjian, D., Taycher, L., Shakhnarovich, G., Graumanand, K., Darrell, T.: Avoiding the streetlight effect: tracking by exploring likelihood modes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 357–364 (2005)

    Google Scholar 

  13. Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. Int. J. Comput. Vis. 61(2), 185–205 (2005)

    Article  Google Scholar 

  14. Fossati, A., Dimitrijevic, M., Lepetit, V., Fua, P.: From canonical poses to 3D motion capture using a single camera. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1165–1181 (2010)

    Article  Google Scholar 

  15. Friborg, R., Hauberg, S., Erleben, K.: GPU accelerated likelihoods for stereo-based articulated tracking. In: European Conference on Computer Vision—Workshop on Computer Vision on GPUs (2010)

    Google Scholar 

  16. Gall, J., Fossati, A., van Gool, L.: Functional categorization of objects using real-time markerless motion capture. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1969–1976 (2011)

    Google Scholar 

  17. Gall, J., Stoll, C., de Aguiar, E., Theobalt, C., Rosenhahn, B., Seidel, H.P.: Motion capture using joint skeleton tracking and surface estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746–1753 (2009)

    Chapter  Google Scholar 

  18. Ganapathi, V., Plagemann, C., Thrun, S., Koller, D.: Real time motion capture using a single time-of-flight camera. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)

    Google Scholar 

  19. Girshick, R.B., Shotton, A., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: IEEE International Conference on Computer Vision, pp. 415–422 (2011)

    Google Scholar 

  20. Grest, D., Krüger, V., Koch, R.: Single view motion tracking by depth and silhouette information. In: Proceedings of the Scandinavian Conference on Image Analysis, pp. 719–729. Springer, Berlin (2007)

    Google Scholar 

  21. Guan, P., Weiss, A., Bălan, A.O., Black, M.J.: Estimating human shape and pose from a single image. In: IEEE International Conference on Computer Vision, pp. 1381–1388 (2009)

    Chapter  Google Scholar 

  22. Hasler, N., Ackermann, H., Rosenhahn, B., Thormählen, T., Seidel, H.P.: Multilinear pose and body shape estimation of dressed subjects from image sets. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1823–1830 (2010)

    Google Scholar 

  23. Heikkila, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1106–1112 (1997)

    Google Scholar 

  24. Knoop, S., Vacek, S., Dillmann, R.: Fusion of 2D and 3D sensor data for articulated body tracking. Robot. Auton. Syst. 57(3), 321–329 (2009)

    Article  Google Scholar 

  25. Kolb, A., Barth, E., Koch, R., Larsen, R.: Time-of-flight sensors in computer graphics. Comput. Graph. Forum 29(1), 141–159 (2010)

    Article  Google Scholar 

  26. Lewis, J.P., Cordner, M., Fong, N.: Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation. In: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH, pp. 165–172. ACM/Addison-Wesley, New York/Reading (2000)

    Google Scholar 

  27. Lindner, M., Schiller, I., Kolb, A., Koch, R.: Time-of-flight sensor calibration for accurate range sensing. Comput. Vis. Image Underst. 114(12), 1318–1328 (2010). Special issue on Time-of-Flight Camera Based Computer Vision

    Article  Google Scholar 

  28. López-Méndez, A., Alcoverro, M., Pardàs, M., Casas, J.R.: Real-time upper body tracking with online initialization using a range sensor. In: International Conference on Computer Vision Workshops, pp. 391–398 (2011)

    Chapter  Google Scholar 

  29. MATLAB camera calibration toolbox. http://www.vision.caltech.edu/bouguetj/calib_doc (2012)

  30. Microsoft: Kinect SDK beta. http://www.microsoft.com/en-us/kinectforwindows (2012)

  31. Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006)

    Article  Google Scholar 

  32. Murray, R.M., Li, Z., Sastry, S.S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)

    MATH  Google Scholar 

  33. Okada, R., Soatto, S.: Relevant feature selection for human pose estimation and localization in cluttered images. In: Proceedings of the European Conference on Computer Vision, pp. 434–445 (2008)

    Google Scholar 

  34. Okada, R., Stenger, B.: A single camera motion capture system for human–computer interaction. IEICE Trans. Inf. Syst. E91-D, 1855–1862 (2008)

    Article  Google Scholar 

  35. Pekelny, Y., Gotsman, C.: Articulated object reconstruction and markerless motion capture from depth video. Comput. Graph. Forum 27(2), 399–408 (2008)

    Article  Google Scholar 

  36. Plagemann, C., Ganapathi, V., Koller, D., Thrun, S.: Realtime identification and localization of body parts from depth images. In: IEEE International Conference on Robotics and Automation (2010)

    Google Scholar 

  37. Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)

    Article  Google Scholar 

  38. Primesense: Primesense NITE middleware. http://www.primesense.com (2012)

  39. Romero, J., Kjellström, H., Kragic, D.: Hands in action: real-time 3D reconstruction of hands in interaction with objects. In: IEEE International Conference on Robotics and Automation, pp. 458–463 (2010)

    Chapter  Google Scholar 

  40. Rosales, R., Sclaroff, S.: Inferring body pose without tracking body parts. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 721–727 (2000)

    Google Scholar 

  41. Rosales, R., Sclaroff, S.: Combining generative and discriminative models in a framework for articulated pose estimation. Int. J. Comput. Vis. 67, 251–276 (2006)

    Article  Google Scholar 

  42. Rosenhahn, B., Schmaltz, C., Brox, T., Weickert, J., Cremers, D., Seidel, H.P.: Markerless motion capture of man–machine interaction. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  43. Salzmann, M., Urtasun, R.: Combining discriminative and generative methods for 3D deformable surface and articulated pose reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  44. Schwarz, L.A., Mateus, D., Castañeda, V., Navab, N.: Manifold learning for ToF-based human body tracking and activity recognition. In: British Machine Vision Conference (2010)

    Google Scholar 

  45. Schwarz, L., Mkhytaryan, A., Mateus, D., Navab, N.: Estimating human 3D pose from time-of-flight images based on geodesic distances and optical flow. In: IEEE Conference on Automatic Face and Gesture Recognition (2011)

    Google Scholar 

  46. Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter-sensitive hashing. In: International Conference on Computer Vision, pp. 750–757 (2003)

    Chapter  Google Scholar 

  47. Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, New York (2002)

    Google Scholar 

  48. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from a single depth image. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)

    Google Scholar 

  49. Siddiqui, M., Medioni, G.: Human pose estimation from a single view point, real-time range sensor. In: Computer Vision and Pattern Recognition Workshops (2010)

    Google Scholar 

  50. Sigal, L., Bălan, A.O., Black, M.J.: Combined discriminative and generative articulated pose and non-rigid shape estimation. In: Advances in Neural Information Processing Systems, pp. 1337–1344 (2008)

    Google Scholar 

  51. Stoll, C., Hasler, N., Gall, J., Seidel, H.P., Theobalt, C.: Fast articulated motion tracking using a sums of gaussians body model. In: International Conference on Computer Vision, pp. 951–958 (2011)

    Chapter  Google Scholar 

  52. Wang, R.Y., Popovic, J.: Real-time hand-tracking with a color glove. ACM Trans. Graph. 28(3) (2009)

    Google Scholar 

  53. Wei, X., Chai, J.: Videomocap: modeling physically realistic human motion from monocular video sequences. ACM Trans. Graph. 29(4), 42:1–42:10 (2010)

    Google Scholar 

  54. Weiss, A., Hirshberg, D., Black, M.J.: Home 3D body scans from noisy image and range data. In: IEEE International Conference on Computer Vision, pp. 1951–1958 (2011)

    Google Scholar 

  55. Ye, M., Wang, X., Yang, R., Ren, L., Pollefeys, M.: Accurate 3D pose estimation from a single depth image. In: International Conference on Computer Vision, pp. 731–738 (2011)

    Chapter  Google Scholar 

  56. Zhu, Y., Dariush, B., Fujimura, K.: Controlled human pose estimation from depth image streams. In: Computer Vision and Pattern Recognition Workshops (2008)

    Google Scholar 

  57. Zhu, Y., Dariush, B., Fujimura, K.: Kinematic self retargeting: a framework for human pose estimation. Comput. Vis. Image Underst. 114(12), 1362–1375 (2010). Special issue on Time-of-Flight Camera Based Computer Vision

    Article  Google Scholar 

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Acknowledgements

This work was supported by the German Research Foundation (DFG CL 64/5-1) and by the Intel Visual Computing Institute. Meinard Müller has been funded by the Cluster of Excellence on Multimodal Computing and Interaction (MMCI) and is now with the University of Bonn.

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Authors and Affiliations

  1. MPI Informatik & Saarland University, Campus E1.4, 66123, Saarbrücken, Germany

    Andreas Baak

  2. Bonn University & MPI Informatik, Römerstraße 164, 53117, Bonn, Germany

    Meinard Müller

  3. MPI Informatik, Campus E1.4, 66123, Saarbrücken, Germany

    Gaurav Bharaj, Hans-Peter Seidel & Christian Theobalt

Authors
  1. Andreas Baak
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  2. Meinard Müller
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  3. Gaurav Bharaj
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  4. Hans-Peter Seidel
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  5. Christian Theobalt
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Correspondence to Andreas Baak .

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Editors and Affiliations

  1. Computer Vision Laboratory, ETH Zürich, Sternwartstrasse 7, Zürich, 8092, Switzerland

    Andrea Fossati

  2. Perceiving Systems Department, Max Planck Inst. for Intelligent Systems, Spemannstrasse 41, Tübingen, 72076, Germany

    Juergen Gall

  3. Computer Vision Laboratory, ETH Zürich, Sternwartstrasse 7, Zürich, 8092, Switzerland

    Helmut Grabner

  4. Intel Science and Technology Center, Allen Center 462, Seattle, 98195, Washington, USA

    Xiaofeng Ren

  5. Industrial Perception, Industrial Ave 911, Palo Alto, 94303, California, USA

    Kurt Konolige

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Baak, A., Müller, M., Bharaj, G., Seidel, HP., Theobalt, C. (2013). A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds) Consumer Depth Cameras for Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4640-7_5

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