Change-driven Image Architecture on FPGA with adaptive threshold for Optical-Flow Computation

Optical flow computation has been extensively used for object motion estimation in image sequences. However, the results obtained by most optical flow techniques are as accurate as computationally intensive due to the large amount of data involved. A new strategy for image sequence processing has been developed; pixels of the image sequence that significantly change fire the execution of the operations related to the image processing algorithm. The data reduction achieved with this strategy allows a significant optical flow computation speed-up. Furthermore, FPGAs allow the implementation of a custom data-flow architecture specially suited for this strategy. The foundations of the change-driven image processing are presented, as well as the hardware custom implementation in an EP20K1000C FPGA showing the achieved performance

[1]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[2]  Shang-Hong Lai,et al.  Accurate optical flow computation under non-uniform brightness variations , 2005, Comput. Vis. Image Underst..

[3]  Avinash C. Kak,et al.  Robust motion estimation under varying illumination , 2005, Image Vis. Comput..

[4]  Jose Antonio Boluda,et al.  FPGA Implementation of a Change-Driven Image Processing Architecture for Optical Flow Computation , 2006, 2006 International Conference on Field Programmable Logic and Applications.

[5]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[6]  Aurélio J. C. Campilho,et al.  A Pipelined Real-Time Optical Flow Algorithm , 2004, ICIAR.

[7]  Antonio García Dopico,et al.  Distributed Computation of Optical Flow , 2004, International Conference on Computational Science.

[8]  Unai Bidarte,et al.  Hardware implementation of optical flow constraint equation using FPGAs , 2005, Comput. Vis. Image Underst..

[9]  Paul L. Rosin,et al.  Evaluation of global image thresholding for change detection , 2003, Pattern Recognit. Lett..

[10]  A. Stocker.  Analog Integrated 2-D Optical Flow Sensor , 2006 .

[11]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[12]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[13]  Abbas El Gamal,et al.  Optical flow estimation using temporally oversampled video , 2005, IEEE Transactions on Image Processing.