A Hardware-Friendly Adaptive Tensor Based Optical Flow Algorithm

  • Zhao-Yi Wei
  • Dah-Jye Lee
  • Brent E. Nelson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


A tensor-based optical flow algorithm is presented in this paper. This algorithm uses a cost function that is an indication of tensor certainty to adaptively adjust weights for tensor computation. By incorporating a good initial value and an efficient search strategy, this algorithm is able to determine optimal weights in a small number of iterations. The weighting mask for the tensor computation is decomposed into rings to simplify a 2D weighting into 1D. The devised algorithm is well-suited for real-time implementation using a pipelined hardware structure and can thus be used to achieve real-time optical flow computation. This paper presents simulation results of the algorithm in software, and the results are compared with our previous work to show its effectiveness. It is shown that the proposed new algorithm automatically achieves equivalent accuracy to that previously achieved via manual tuning of the weights.


Optical Flow Structure Tensor Active Contour Model Weighting Process Mask Size 
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 2007

Authors and Affiliations

  • Zhao-Yi Wei
    • 1
  • Dah-Jye Lee
    • 1
  • Brent E. Nelson
    • 1
  1. 1.Department of Electrical and Computer Engineering, Brigham Young University, Provo, UTUSA

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