A Reliable FPGA-based Real-time Optical-flow Estimation
Optical flow is a research topic of interest for many
years. It has, until recently, been largely inapplicable to real-time
applications due to its computationally expensive nature. This paper
presents a new reliable flow technique which is combined with a
motion detection algorithm, from stationary camera image streams,
to allow flow-based analyses of moving entities, such as rigidity, in
real-time. The combination of the optical flow analysis with motion
detection technique greatly reduces the expensive computation of
flow vectors as compared with standard approaches, rendering the
method to be applicable in real-time implementation. This paper
describes also the hardware implementation of a proposed pipelined
system to estimate the flow vectors from image sequences in real
time. This design can process 768 x 576 images at a very high frame
rate that reaches to 156 fps in a single low cost FPGA chip, which is
adequate for most real-time vision applications.
[1] C. Anderson, P. Burt, and G. van der Wal, "Change detection and
tracking using pyramid transformation techniques", in Proceedings of
SPIE. Intelligent Robots and Computer Vision, vol. 579, pp. 72-78,
1985.
[2] J. Barron, D. Fleet, and S. Beauchemin, "Performance of optical flow
techniques", International Journal of Computer Vision, vol. 12, pp. 42-
77, 1994.
[3] A. Kasinski and A. Hamdy, "Efficient Separation of mobile objects on
the scene from the sequence taken with an overhead camera". Proc. Int.
Conf. on Computer Vision and Graphics, Zakopane, vol. 1, pp. 425-430,
Sept. 2002.
[4] C. Ridder, O. Munkelt, and H. Kirchner, "Adaptive Background
Estimation and Foreground Detection Using Kalman-Filtering". Proc.
Int. l Conf. Recent Advances in Mechatronics, ICRAM .95, pp. 193-199,
1995.
[5] Y. Ivanov, A. Bobick, and J. Liu, "Fast Lighting Independent
Background Subtraction". Technical Report no. 437, MIT Media
Laboratory, 1997.
[6] G. Halevy and D. Weinshall, "Motion of disturbances: detection and
tracking of multi-body non-rigid motion", Machine Vision and
Applications, vol. 11, Issue 3, pp. 122-137, 1999.
[7] E.M. Saad, A. Hamdy, and M.M. Abutaleb, "FPGA-based
Implementation of a Low Cost and Area Real-time Motion Detection",
15th IEEE Conference in Mixed Design of Integrated Circuits and
Systems, Poznan, Poland, pp. 249-254, June 19-21, 2008.
[8] P. Anandan, "A Computational Framework and an Algorithm for the
Measurement of Visual Motion", International Journal of Computer
Vision, vol. 2, 1989.
[9] M. Okutomi and T. Kanade, "A Locally Adaptive Window for Signal
Matching", International Journal of Computer Vision, vol. 7, no. 2,
1994.
[10] A. J. Lipton, "Local Application of Optic Flow to Analyze Rigid versus
Non-Rigid Motion", ICCV Workshop on Frame-Rate Vision, 1999.
[11] K. N. Ngan, T. Meier, and D. Chai, "Advanced Video Coding: Principles
and Techniques", Elsevier, 1999.
[12] B. Horn, B. Schunck, "Determining optical flow", Artificial Intelligence,
vol. 17, pp. 185-203, 1981.
[13] J. L. Martín, A. Zuloaga, C. Cuadrado, J. Lázaro, and U. Bidarte,
"Hardware implementation of optical flow constraint equation using
FPGAs", Computer Vision and Image Understanding, vol. 98, pp. 462-
490, 2005.
[14] B. D. Lucas, T. Kanade, "An iterative image registration technique with
an application to stereo vision", Proc. DARPA Image understanding
Workshop, pp. 121-130, 1984.
[15] J. Díaz, E. Ros, F. Pelayo, E. M. Ortigosa, and S. Mota, "FPGA-based
real-time optical-flow system", IEEE Trans. Circuits and Systems for
Video Technology, vol. 16, no. 2, pp. 274-279, Feb 2006.
[16] Z.Y. Wei, D.J. Lee, B.E. Nelson, and M.A. Martineau, "A fast and
accurate tensor-based optical flow algorithm implemented in FPGA",
IEEE WACV, Austin, Texas, USA, p. 18 (6 pages), Feb 21-22, 2007.
[17] M.M. Abutaleb, A. Hamdy, and E.M. Saad, "FPGA-Based Real-Time
Video-Object Segmentation with Optimization Schemes", International
Journal of Circuits, Systems, and Signal Processing, vol. 2, issue 2, pp.
78-86, 2008.
[1] C. Anderson, P. Burt, and G. van der Wal, "Change detection and
tracking using pyramid transformation techniques", in Proceedings of
SPIE. Intelligent Robots and Computer Vision, vol. 579, pp. 72-78,
1985.
[2] J. Barron, D. Fleet, and S. Beauchemin, "Performance of optical flow
techniques", International Journal of Computer Vision, vol. 12, pp. 42-
77, 1994.
[3] A. Kasinski and A. Hamdy, "Efficient Separation of mobile objects on
the scene from the sequence taken with an overhead camera". Proc. Int.
Conf. on Computer Vision and Graphics, Zakopane, vol. 1, pp. 425-430,
Sept. 2002.
[4] C. Ridder, O. Munkelt, and H. Kirchner, "Adaptive Background
Estimation and Foreground Detection Using Kalman-Filtering". Proc.
Int. l Conf. Recent Advances in Mechatronics, ICRAM .95, pp. 193-199,
1995.
[5] Y. Ivanov, A. Bobick, and J. Liu, "Fast Lighting Independent
Background Subtraction". Technical Report no. 437, MIT Media
Laboratory, 1997.
[6] G. Halevy and D. Weinshall, "Motion of disturbances: detection and
tracking of multi-body non-rigid motion", Machine Vision and
Applications, vol. 11, Issue 3, pp. 122-137, 1999.
[7] E.M. Saad, A. Hamdy, and M.M. Abutaleb, "FPGA-based
Implementation of a Low Cost and Area Real-time Motion Detection",
15th IEEE Conference in Mixed Design of Integrated Circuits and
Systems, Poznan, Poland, pp. 249-254, June 19-21, 2008.
[8] P. Anandan, "A Computational Framework and an Algorithm for the
Measurement of Visual Motion", International Journal of Computer
Vision, vol. 2, 1989.
[9] M. Okutomi and T. Kanade, "A Locally Adaptive Window for Signal
Matching", International Journal of Computer Vision, vol. 7, no. 2,
1994.
[10] A. J. Lipton, "Local Application of Optic Flow to Analyze Rigid versus
Non-Rigid Motion", ICCV Workshop on Frame-Rate Vision, 1999.
[11] K. N. Ngan, T. Meier, and D. Chai, "Advanced Video Coding: Principles
and Techniques", Elsevier, 1999.
[12] B. Horn, B. Schunck, "Determining optical flow", Artificial Intelligence,
vol. 17, pp. 185-203, 1981.
[13] J. L. Martín, A. Zuloaga, C. Cuadrado, J. Lázaro, and U. Bidarte,
"Hardware implementation of optical flow constraint equation using
FPGAs", Computer Vision and Image Understanding, vol. 98, pp. 462-
490, 2005.
[14] B. D. Lucas, T. Kanade, "An iterative image registration technique with
an application to stereo vision", Proc. DARPA Image understanding
Workshop, pp. 121-130, 1984.
[15] J. Díaz, E. Ros, F. Pelayo, E. M. Ortigosa, and S. Mota, "FPGA-based
real-time optical-flow system", IEEE Trans. Circuits and Systems for
Video Technology, vol. 16, no. 2, pp. 274-279, Feb 2006.
[16] Z.Y. Wei, D.J. Lee, B.E. Nelson, and M.A. Martineau, "A fast and
accurate tensor-based optical flow algorithm implemented in FPGA",
IEEE WACV, Austin, Texas, USA, p. 18 (6 pages), Feb 21-22, 2007.
[17] M.M. Abutaleb, A. Hamdy, and E.M. Saad, "FPGA-Based Real-Time
Video-Object Segmentation with Optimization Schemes", International
Journal of Circuits, Systems, and Signal Processing, vol. 2, issue 2, pp.
78-86, 2008.
@article{"International Journal of Electrical, Electronic and Communication Sciences:60745", author = "M. M. Abutaleb and A. Hamdy and M. E. Abuelwafa and E. M. Saad", title = "A Reliable FPGA-based Real-time Optical-flow Estimation", abstract = "Optical flow is a research topic of interest for many
years. It has, until recently, been largely inapplicable to real-time
applications due to its computationally expensive nature. This paper
presents a new reliable flow technique which is combined with a
motion detection algorithm, from stationary camera image streams,
to allow flow-based analyses of moving entities, such as rigidity, in
real-time. The combination of the optical flow analysis with motion
detection technique greatly reduces the expensive computation of
flow vectors as compared with standard approaches, rendering the
method to be applicable in real-time implementation. This paper
describes also the hardware implementation of a proposed pipelined
system to estimate the flow vectors from image sequences in real
time. This design can process 768 x 576 images at a very high frame
rate that reaches to 156 fps in a single low cost FPGA chip, which is
adequate for most real-time vision applications.", keywords = "Optical flow, motion detection, real-time systems,FPGA.", volume = "4", number = "3", pages = "602-6", }
