Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Farnebäck, G.: Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. In: Proc. ICCV, vol. 1, pp. 77–80 (2001)
Farnebäck, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: Proc. ICPR., vol. 1, pp. 135–139 (2000)
Liu, H., Chellappa, R., Rosenfeld, A.: Accurate dense optical flow estimation using adaptive structure tensors and a parametric model. IEEE Trans. Image Processing 12, 1170–1180 (2003)
Haussecker, H., Spies, H.: Handbook of Computer Vision and Application, vol. 2, ch. 13, Academic, New York (1999)
Wang, H., Ma, K.: Structure tensor-based motion field classification and optical flow estimation. In: Proc. ICICS-PCM, vol. 1, pp. 66–70 (2003)
Previous publication hidden for blind review
Correia, M., Campilho, A.: Real-time implementation of an optical flow algorithm. In: Proc. ICIP, vol. 4, pp. 247–250 (2002)
Zuloaga, A., MartÃn, J.L, Ezquerra, J.: Hardware architecture for optical flow estimation in real time. In: Proc. ICIP, vol. 3, pp. 972–976 (1998)
MartÃn, J.L., Zuloaga, A., Cuadrado, C., Lázaro, J., Bidarte, U.: Hardware implementation of optical flow constraint equation using FPGAs. In: Computer Vision and Image Understanding, vol. 98, pp. 462–490 (2005)
DÃaz, J., Ros, E., Pelayo, F., Ortigosa, E.M., Mota, S.: FPGA-based real-time optical-flow system. IEEE Trans. Circuits and Systems for Video Technology 16(2), 274–279 (2006)
Middendorf, M., Nagel, H.–H.: Estimation and interpretation of discontinuities in optical flow fields. In: Proc. ICCV, vol. 1, pp. 178–183 (2001)
Kühne, G., Weickert, J., Schuster, O., Richter, S.: A tensor-driven active contour model for moving object segmentation. In: Proc. ICIP, vol. 2, pp. 73–76 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wei, ZY., Lee, DJ., Nelson, B.E. (2007). A Hardware-Friendly Adaptive Tensor Based Optical Flow Algorithm. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-76856-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76855-5
Online ISBN: 978-3-540-76856-2
eBook Packages: Computer ScienceComputer Science (R0)