Skip to main content

Human Behavior Analysis from Depth Maps

  • Conference paper
Articulated Motion and Deformable Objects (AMDO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7378))

Included in the following conference series:

Abstract

Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect\(^{\textsf{TM}}\) sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Jain, H., Subramanian, A.: Real-time upper-body human pose estimation using a depth camera, HP Technical Reports

    Google Scholar 

  2. Rodgers, J., Anguelov, D., Hoi-Cheung, P.: Object pose detection in range scan data. In: CVPR, pp. 2445–2452 (2006)

    Google Scholar 

  3. Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: CVPR, pp. 755–762 (2010)

    Google Scholar 

  4. Sabata, B., Arman, F., Aggarwal, J.: Segmentation of 3d range images using pyramidal data structures. CVGIP: Image Understanding 57(3), 373–387 (1993)

    Article  Google Scholar 

  5. Primesensor\(\texttrademark\), http://www.primesense.com/?p=514

  6. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M.: Real-time human pose recognition in parts from single depth images (2011)

    Google Scholar 

  7. Dephsense ds311, http://www.softkinetic.com/Solutions/DepthSensecameras.aspx

  8. Openni, http://www.openni.org

  9. Flexible action and articulated skeleton toolkit (faast), http://projects.ict.usc.edu/mxr/faast/

  10. Suma, E., Lange, B., Rizzo, A., Krum, D.M.: FAAST: the flexible action and articulated skeleton toolkit. In: Virtual Reality, Singapore, pp. 245–246 (2011)

    Google Scholar 

  11. Kinect for windows sdk from microsoft research, http://research.microsoft.com/en-us/um/redmond/projects/kinectsdk/

  12. Openkinect (libfreenect), http://openkinect.org/

  13. Code laboratories cl nui platform - kinect driver/sdk, http://codelaboratories.com/nui/

  14. Point cloud library (pcl), http://pointclouds.org/

  15. Rusu, R.B.: Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. Articial Intelligence (KI-Kuenstliche Intelligenz) (2010)

    Google Scholar 

  16. Lai, K., Bo, L., Ren, X., Fox, D.: Sparse distance learning for object recognition combining rgb and depth information. In: ICRA

    Google Scholar 

  17. Bo, L., Ren, X., Fox, D.: Depth kernel descriptors for object recognition. In: IROS, pp. 821–826 (2011)

    Google Scholar 

  18. Koch, R., Schiller, I., Bartczak, B., Kellner, F., Koser, K.: Mixin3d: 3d mixed reality with tof-camera, pp. 126–141 (2009)

    Google Scholar 

  19. Castaneda, V., Mateus, D., Navab, N.: Slam combining tof and high-resolution cameras. In: WACV, pp. 672–678 (2011)

    Google Scholar 

  20. Gehrig, D., Kuehne, H.: Hmm-based human motion recognition with optical flow data. In: IEEE International Conference on Humanoid Robots, Humanoids 2009 (2009)

    Google Scholar 

  21. Sminchisescu, C., Kanaujia, A., Metaxas, D.: Conditional models for contextual human motion recognition. CVIU 104(2-3), 210–220 (2006)

    Google Scholar 

  22. Zhou, F., la Torre, F.D., Hodgins, J.K.: Aligned cluster analysis for temporal segmentation of human motion. In: IEEE Conference on Automatic Face and Gestures Recognition, FG (2008)

    Google Scholar 

  23. Reyes, M., Dominguez, G., Escalera, S.: Feature weighting in dynamic time warping for gesture recognition in depth data. In: ICCV, Barcelona, Spain (2011)

    Google Scholar 

  24. Hernandez-Vela, A., Zlateva, N., Marinov, A., Reyes, M., Radeva, P., Dimov, D., Escalera, S.: Graph cuts optimization for multi-limb human segmentation in depth maps. In: CVPR (2012)

    Google Scholar 

  25. Hernandez-Vela, A., Reyes, M., Escalera, S., Radeva, P.: Spatio-temporal grabcut human segmentation for face and pose recovery. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, CVPR (2010)

    Google Scholar 

  26. Hernandez-Vela, A., Primo, C., Escalera, S.: Automatic user interaction correction via multi-label graph cuts. In: 1st IEEE International Workshop on Human Interaction in Computer Vision HICV, ICCV (2011)

    Google Scholar 

  27. Igual, L., Soliva, J., Hernandez-Vela, A., Escalera, S., Jimenez, X., Vilarroya, O., Radeva, P.: A fully-automatic caudate nucleus segmentation of brain mri: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder. In: BioMedical Engineering OnLine (2011)

    Google Scholar 

  28. Liu, Y., Stoll, C., Gall, J., Seidel, H.: Markerless motion capture of interacting characters using multi-view image segmentation. CVPR 14(1), 1249–1256 (2011)

    Article  Google Scholar 

  29. Holt, B., Ong, E.-J., Cooper, H., Bowden, R.: Putting the pieces together: Connected poselets for human pose estimation. In: ICCV (2011)

    Google Scholar 

  30. Pugeault, N., Bowden, R.: Spelling it out: Real-time asl fingerspelling recognition. In: ICCV (2011)

    Google Scholar 

  31. Plagemann, C., Ganapathi, V., Koller, D., Thrun, S.: Real-time identification and localization of body parts from depth images. In: ICCV, pp. 3108–3113 (2011)

    Google Scholar 

  32. Clapes, A., Reyes, M., Escalera, S.: User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds.) AMDO 2012. LNCS, vol. 7378, pp. 1–11. Springer, Heidelberg (2012)

    Google Scholar 

  33. Charles, J., Everingham, M.: Learning shape models for monocular human pose estimation from the microsoft xbox kinect. In: ICCV, pp. 1202–1208 (2011)

    Google Scholar 

  34. Bo, L., Lai, K., Ren, X., Fox, D.: Object recognition with hierarchical kernel descriptors. In: CVPR (2011)

    Google Scholar 

  35. Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: A quantum mechanical approach to shape analysis. In: ICCV (2011)

    Google Scholar 

  36. Schwarz, L., Mkhitaryan, 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, FG (2011)

    Google Scholar 

  37. Ganapathiand, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: CVPR, pp. 755–762 (2010)

    Google Scholar 

  38. Keskin, C., Racc, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: ICCV (2011)

    Google Scholar 

  39. Minnen, D., Zafrulla, Z.: Towards robust cross-user hand tracking and shape recognition. In: ICCV, pp. 1235–1241 (2011)

    Google Scholar 

  40. Windheuser, T., Schlickewei, U., Schmidt, F.R.: Geometrically consistent elastic matching of 3d shapes: A linear programming solution. In: ICCV (2011)

    Google Scholar 

  41. Xia, L., Chen, C.-C., Aggarwal, J.K.: Human detection using depth information by kinect department of electrical and computer engineering. PR, 15–22 (2011)

    Google Scholar 

  42. Human pose recovery and behavior analysis group, http://www.maia.ub.es/~sergio/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Escalera, S. (2012). Human Behavior Analysis from Depth Maps. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds) Articulated Motion and Deformable Objects. AMDO 2012. Lecture Notes in Computer Science, vol 7378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31567-1_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31567-1_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31566-4

  • Online ISBN: 978-3-642-31567-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics