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Real-Time Estimation of Optical Flow Based on Optimized Haar Wavelet Features

  • Jan Salmen
  • Lukas Caup
  • Christian Igel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)

Abstract

Estimation of optical flow is required in many computer vision applications. These applications often have to deal with strict time constraints. Therefore, flow algorithms with both high accuracy and computational efficiency are desirable. Accordingly, designing such a flow algorithm involves multi-objective optimization. In this work, we build on a popular algorithm developed for real-time applications. It is originally based on the Census transform and benefits from this encoding for table-based matching and tracking of interest points. We propose to use the more universal Haar wavelet features instead of the Census transform within the same framework. The resulting approach is more flexible, in particular it allows for sub-pixel accuracy. For comparison with the original method and another baseline algorithm, we considered both popular benchmark datasets as well as a long synthetic video sequence. We employed evolutionary multi-objective optimization to tune the algorithms. This allows to compare the different approaches in a systematic and unbiased way. Our results show that the overall performance of our method is significantly higher compared to the reference implementation.

Keywords

Optical Flow Pareto Front Multiobjective Optimization Candidate Solution Interest Point 
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 2011

Authors and Affiliations

  • Jan Salmen
    • 1
  • Lukas Caup
    • 1
  • Christian Igel
    • 2
  1. 1.Institut für NeuroinformatikRuhr-Universität BochumBochumGermany
  2. 2.Department of Computer ScienceUniversity of CopenhagenCopenhagen ØDenmark

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