Optical flow estimation: Advances and comparisons

  • M. Otte
  • H. -H. Nagel
Optical Flow and Motion Fields
Part of the Lecture Notes in Computer Science book series (LNCS, volume 800)


This contribution investigates local differential techniques for estimating optical flow and its derivatives based on the brightness change constraint. By using the tensor calculus representation we build the Taylor expansion of the gray-value derivatives as well as of the optical flow in a spatiotemporal neighborhood. Such a formulation simplifies a unifying framework for all existing local differential approaches and allows to derive new systems of equations to estimate the optical flow and its derivatives. We also tested various optical flow estimation approaches on real image sequences recorded by a calibrated camera fixed on the arm of a robot. By moving the arm of the robot along a precisely defined trajectory we can determine the true displacement rate of scene surface elements projected into the image plane and compare it quantitatively with the results of different optical flow estimators.


Optical Flow Difference Vector Angular Error Rigorous Condition Optical Flow Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • M. Otte
    • 1
  • H. -H. Nagel
    • 1
    • 2
  1. 1.Institut für Algorithmen und Kognitive SystemeFakultät für Informatik der Universität Karlsruhe (TH)KarlsruheGermany
  2. 2.Fraunhofer - Institut für Informations- und Datenverarbeitung (IITB)Karlsruhe

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