Markerless Tracking/Related Work

This page sumarizes related work for the research project Markerless Tracking.

Recognition edit

Blanz et. al. showed how to use computer generated images of faces to recognize real ones.[1] This work is based on a morphable face model which was presented before.[2]

Not jet reviewd: [3]

Tracking edit

Motion Capturing could greatly improve with analysis-by-synthesis as stated by Moeslund and Granum.[4]

Not jet reviewd: [5] [6] [7]

Theory edit

Not jet reviewd: [8]

Commercial edit

Organic Motion's solution to Motion Capturing was presented at SIGGRAPH 2007 (demo video) and is the first commercial available product to track all bodyparts including bones, geometry and textures. Their website claims that "Tschesnok created a thinking system which looks at people in a manner very similar to the way the brain process human vision."[9]. Obviously it is not quite clear how this works. Hopefully their pending patent will be available soon.

Interdisciplinary edit

As briefly described in [10] neuro-scientists found evidence that the nervous system use probabilistic population codecs. [11]

References edit

  1. Face recognition based on fitting a 3D morphable model by Blanz, V. and Vetter, T. in IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1063-1074, year=2003
  2. A morphable model for the synthesis of 3D faces by Blanz, V. and Vetter, T. in Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages: 187-194, year: 1999, publisher: ACM Press/Addison-Wesley Publishing Co. New York, NY, USA
  3. Face recognition: A literature survey by W. Zhao and R. Chellappa and P. J. Phillips and A. Rosenfeld in ACM Comput. Surv. web: ACM Portal
  4. A survey of computer vision-based human motion capture from Moeslund, T.B. and Granum, E in Computer Vision and Image Understanding, volume: 81, number: 3, pages: 231-268, year: 2001
  5. The convergence of graphics and vision from Lengyel, J., in Computer, volume: 31, number: 7, pages: 46-53, year: 1998
  6. Object tracking: A survey by Alper Yilmaz and Omar Javed and Mubarak Shah in ACM Comput. Surv., web: ACM Portal
  7. Adaptable Model-Based Tracking Using Analysis-by-Synthesis Techniques by Harald Wuest, Folker Wientapper and Didier Stricker in Computer Analysis of Images and Patterns, year: 2007, doi: 10.1007/978-3-540-74272-2_3 , web: Springer
  8. Example Based Image Analysis and Synthesis from Beymer, D. and Shashua, A. and Poggio, T.,1993
  9. Organic Motion Website -- Technology from Organic Motion Inc., (accessed 15. Okt 2007)
  10. Das Rauschen im Gehirn from Florian Rötzer in Telepolis online, 13.11.2006, (accessed 07. Sep 2007)
  11. Bayesian inference with probabilistic population codes from Wei Ji Ma and Jeffrey M Beck and Peter E Latham and Alexandre Pouget in Nature Neuroscience, volume 9, pages 1349--1350, year 2006