Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-08

AUTHORS

C. Y. Ren, V. A. Prisacariu, O. Kähler, I. D. Reid, D. W. Murray

ABSTRACT

We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depth-colour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection. More... »

PAGES

80-95

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1992-08. Robust model-based motion tracking through the integration of search and estimation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012-07. PWP3D: Real-Time Segmentation and Tracking of 3D Objects in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. A Unified Energy Minimization Framework for Model Fitting in Depth in COMPUTER VISION – ECCV 2012. WORKSHOPS AND DEMONSTRATIONS
  • 1992-04. Visual tracking of known three-dimensional objects in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2013. Simultaneous Monocular 2D Segmentation, 3D Pose Recovery and 3D Reconstruction in COMPUTER VISION – ACCV 2012
  • 2013. CopyMe3D: Scanning and Printing Persons in 3D in PATTERN RECOGNITION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11263-016-0978-2

    DOI

    http://dx.doi.org/10.1007/s11263-016-0978-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016855106


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