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Human Centered Scene Understanding Based on Depth Information – How to Deal with Noisy Skeleton Data?

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

Scene understanding is a challenging task and and mainly based on geometric or object centered approaches. Hence, the aim of this paper to introduce a novel human centered approach for scene analysis and tackle challenges of noisy long-term tracking data obtained by a depth sensor. Hence, fast filtering mechanisms are proposed to filter noisy tracking data, reducing the number of outliers and thus significantly improving the accuracy of the detection of walking and sitting areas within indoor environments. Evaluation is performed on two different scenes containing 18 and 34 days of tracking data and shows that detecting and filtering invalid tracking information dramatically increases the accuracy.

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Author information

Authors and Affiliations

  1. Computer Vision Lab, Vienna University of Technology, Favoritenstrasse 9-11/183-2, A-1040, Vienna, Austria

    Rainer Planinc & Martin Kampel

Authors
  1. Rainer Planinc
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  2. Martin Kampel
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Planinc, R., Kampel, M. (2014). Human Centered Scene Understanding Based on Depth Information – How to Deal with Noisy Skeleton Data?. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_58

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_58

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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