In this chapter we review the estimation of the two-dimensional apparent motion field of two consecutive images in an image sequence. This apparent motion field is referred to as optical flow field, a two-dimensional vector field on the image plane. Because it is nearly impossible to cover the vast amount of approaches in the literature, in this chapter we set the focus on energy minimization approaches which estimate a dense flow field. The term dense refers to the fact that a flow vector is assigned to every (non-occluded) image pixel. Most dense approaches are based on the variational formulation of the optical flow problem, firstly suggested by Horn and Schunk. Depending on the application, density might be one important property besides accuracy and robustness. In many cases computational speed and real-time capability is a crucial issue. In this chapter we therefore discuss the latest progress in accuracy, robustness and real-time capability of dense optical flow algorithms.


Optical Flow Data Term Flow Vector Pyramid Level Optical Flow Field 
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 London Limited 2011

Authors and Affiliations

  1. 1.Group ResearchDaimler AGSindelfingenGermany
  2. 2.Department of Computer ScienceTechnical University of MunichGarchingGermany

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