Abstract
The last few years have seen a surge in the development of natural user interfaces. These interfaces do not require devices such as keyboards and mice that have been the dominant modes of interaction over the last few decades. An important milestone in the progress of natural user interfaces was the recent launch of Kinect with its unique ability to reliably estimate the pose of the human user in real time. Human pose estimation has been the subject of much research in Computer Vision, but only recently with the introduction of depth cameras and algorithmic advances has pose estimation made it out of the lab and into the living room. In this chapter we briefly summarize the work on human pose estimation for Kinect that has been undertaken at Microsoft Research Cambridge, and discuss some of the remaining open challenges. Due to the summary nature of this chapter, we limit our description to the key insights and refer the reader to the original publications for the technical details.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This project would eventually be launched as Kinect.
- 2.
For more detail on the story behind Kinect, please see the Foreword.
References
Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Ng, A.: Discriminative learning of Markov random fields for segmentation of 3D scan data. In: IEEE Conference on Computer Vision and Pattern Recognition (2005)
Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. (1992). doi:10.1109/34.121791
Blake, A., Kohli, P.: Introduction to Markov Random Fields. Markov Random Fields for Vision and Image Processing. MIT Press, Cambridge (2011)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61(1), 55–79 (2005)
Gall, J., Lempitsky, V.: Class-specific Hough forests for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: International Conference on Computer Vision (2011)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. Int. J. Comput. Vis. 77(1–3), 259–289 (2008)
Moeslund, T., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. (2006). doi:10.1016/j.cviu.2006.08.002
Müller, J., Arens, M.: Human pose estimation with implicit shape models. In: ARTEMIS (2010)
Poppe, R.: Vision-based human motion analysis: an overview. Comput. Vis. Image Underst. 108 (2007). doi:10.1016/j.cviu.2006.10.016
Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter sensitive hashing. In: International Conference on Computer Vision (2003)
Sharp, T.: Implementing decision trees and forests on a GPU. In: European Conference on Computer Vision (2008)
Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)
Sun, M., Kohli, P., Shotton, J.: Conditional regression forests for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
Taylor, J., Shotton, J., Sharp, T., Fitzgibbon, A.: The Vitruvian Manifold: inferring dense correspondences for one-shot human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)
Acknowledgements
This chapter is a summary of existing published work, and we would like to highlight the contributions of all the original authors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Kohli, P., Shotton, J. (2013). Key Developments in Human Pose Estimation for Kinect. 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_4
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4640-7_4
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4639-1
Online ISBN: 978-1-4471-4640-7
eBook Packages: Computer ScienceComputer Science (R0)