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Synchronized Video and Motion Capture Dataset and Quantitative Evaluation of Vision Based Skeleton Tracking Methods for Robotic Action Imitation

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Information and Communication Technology for Development for Africa (ICT4DA 2017)

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Abstract

Marker-less skeleton tracking methods are being widely used for applications such as computer animation, human action recognition, human robot collaboration and humanoid robot motion control. Regarding robot motion control, using the humanoid’s 3D camera and a robust and accurate tracking algorithm, vision based tracking could be a wise solution. In this paper we quantitatively evaluate two vision based marker-less skeleton tracking algorithms (the first, Igalia’s Skeltrack skeleton tracking and the second, an adaptable and customizable method which combines color and depth information from the Kinect.) and perform comparative analysis on upper body tracking results. We have generated a common dataset of human motions by synchronizing an XSENS 3D Motion Capture System, which is used as a ground truth data and a video recording from a 3D sensor device. The dataset, could also be used to evaluate other full body skeleton tracking algorithms. In addition, sets of evaluation metrics are presented.

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Authors and Affiliations

  1. Bahir Dar Institute of Technology, Bahir Dar, Ethiopia

    Selamawet Atnafu

  2. University of Trento, Trento, Italy

    Conci Nicola

Authors
  1. Selamawet Atnafu
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  2. Conci Nicola
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Corresponding author

Correspondence to Selamawet Atnafu .

Editor information

Editors and Affiliations

  1. Council for Scientific and Industrial Research, Pretoria, South Africa

    Fisseha Mekuria

  2. Information Technology, University of Turku, Turku, Finland

    Ethiopia Enideg Nigussie

  3. Dresden University of Technology, Dresden, Germany

    Waltenegus Dargie

  4. Aalto University, Helsinki, Finland

    Mutafugwa Edward

  5. Bahir Dar Institute of Technology, Bahir Dar, Ethiopia

    Tesfa Tegegne

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Atnafu, S., Nicola, C. (2018). Synchronized Video and Motion Capture Dataset and Quantitative Evaluation of Vision Based Skeleton Tracking Methods for Robotic Action Imitation. In: Mekuria, F., Nigussie, E., Dargie, W., Edward, M., Tegegne, T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-95153-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-95153-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95152-2

  • Online ISBN: 978-3-319-95153-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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