RETIMAC: Real-time motion analysis chip

IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing

Volumn 45, Issue 3, 1998, Pages 361-375

  Nesi, Paolo ^a,b   Innocenti, Fabrizio ^a   Pezzati, Paolo ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b Hi Tech Agency CESVIT Spa   (Italy)

Author keywords

ASIC; Computer vision; Motion estimation; Optical flow estimation; Real time implementation

Indexed keywords

APPLICATION SPECIFIC INTEGRATED CIRCUITS; COMPUTER VISION; IMAGE ANALYSIS; OPTICAL FLOWS; REAL TIME SYSTEMS;

MOTION ESTIMATION; REAL TIME MOTION ANALYSIS CHIP (RETIMAC);

MICROPROCESSOR CHIPS;

EID: 0032028003     PISSN: 10577130     EISSN: None     Source Type: Journal    
DOI: 10.1109/82.664242     Document Type: Article

References (63)

1. A. Verri and T. Poggio, Motion field and optical flow: Qualitative properties, IEEE Trans. Pattern Anal. Machine Intel!., vol. 11, pp. 490-498, May 1989.
2. A. Del Bimbo, P. Nesi, and J. L. C. Sanz, Analysis of optical flow constraints, IEEE Trans. Image Processing, vol. 4, pp. 460-4169, Apr. 1995.
3. P. Nesi, Real-time motion analysis, in Real-Time Imaging: Theory, Techniques, and Applications, P. Laplante and A. Stoyenko, Eds. New York: IEEE, May 1996, pp. 27-57.
4. M. Bertero, T. A. Poggio, and V. Torre, Ill-posed problems in early vision, Proc. IEEE, vol. 76, pp. 869-889, Aug. 1988.
5. P. Nesi, Variational approach for optical flow estimation managing discontinuities, Image Vis. Computing, vol. 11, no. 7, pp. 419-4139, 1993.
6. A. Del Bimbo, P. Nesi, and J. L. C. Sanz, Optical flow computation using extended constraints, IEEE Trans. Image Processing, vol. 5, pp. 701-719, 1996.
7. H. G. Musmann, P. Pirsh, and H.-J. Grallert, Advances in picture coding, Proc. IEEE, vol. 73, pp. 523-548, Apr. 1985.
8. P. Dufaux and F. Moscheni, Motion estimation techniques for digital tv: A review and a new contribution, Proc. IEEE, vol. 83, pp. 858-876, June 1995.
9. J. K. Aggarwal and N. Nandhakumar, On the computation of motion from sequences of images-A review, Proc. IEEE, vol. 76, pp. 917-935, Aug. 1988.
10. A. Artieri and F. Jutand, A versatile and powerful ship for real-time motion estimation, in Proc. ICASSP'89, Glasgow, Scotland, 1989, pp. 2453-2456.
11. K.-M. Yang, M.-T. Sun, and L. Wu, A family of VLSI design for the motion compensation block-matching algorithm, IEEE Trans. Circuits Syst.. vol. 36, pp. 1317-1325, Oct. 1989.
12. L. DeVos and M. Schöbinger, VLSI architecture for a flexible block matching processor, IEEE Trans. Circuits Syst. Video TechnoL, vol. 5, pp. 417-428, Oct. 1995.
13. A. Borri, G. Bucci, and P. Nesi, A robust tracking of 3d motion, in Proc. Euro. Conf. Computer Vision, ECCV'94, Stockholm, Sweden, May 2-6, 1994, pp. 181-188.
14. A. Del Bimbo and P. Nesi, Optical flow estimation on connection machine-2, in Proc. Int. Workshop Computer Architecture Machine Perception, CAMP'93, New Orleans, LA, Dec. 15-17, 1993.
15. P. Danielsson, P. Emanuelsson, K. Chen, P. Ingelhag, and C. Svensson, Single-chip high-speed computation of optical flow, in Proc. Int. Workshop Machine Vision Appl. MVA'90 IAPR, Tokyo, Japan, Nov. 28-30, 1990, pp. 331-335.
16. P. Nesi and A. Del Bimbo, A vision system for estimating people flow, in Image Technology: Advances in Image Processing, Multimedia and Machine Vision, J. L. C. Sanz, Ed. New York: Springer-Verlag, 1996, pp. 179-201.
17. B. K. P. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, vol. 17, pp. 185-203, 1981.
18. A. Singh, Optic Flow Computation: A Unified Perspective. Los Alamitos, CA: IEEE Comput. Society Press, 1991.
19. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, Performance of optical flow techniques, Int. J. Comput. Vis., vol. 12, no. 1, pp. 43-77, 1994.
20. J. P. H. van Santen and G. Sperling, Temporal covariance model of human motion perception, J. Opt. Soc. Amer. A, vol. 1, pp. 451-4173, May 1984.
21. E. H. Adelson and J. R. Bergen, Spatiotemporal energy models for the perception of motion, J. Opt. Soc. Amer. A, vol. 2, pp. 284-299, Feb. 1985.
22. D. Heeger, Model for the extraction of image flow, J. Opt. Soc. Amer. A, vol. 4, pp. 1455-1471, Aug. 1987.
23. A. B. Watson and A. J. Ahumada, Jr., Model of human visual-motion sensing, J. Opt. Soc. Amer. A, vol. 2, pp. 322-341, Feb. 1985.
24. L. Jacobson and H. Wechsler, Derivation of optical flow using a spatiotemporal-frequency approach, Comput. Vis., Graph., Image Processing, vol. 38, pp. 29-65, 1987.
25. P. Anandan, A unified perspective on computational techniques for the measurement of visual motion, in Proc. 1st IEEE Int. Conf. Computer Vision ICCV'87, London, England, June 8-11, 1987, pp. 219-230.
26. P. J. Burt, C. Yen, and X. Xu, Multiresolution flow-through motion analysis, in Proc. of the IEEE Conf. Computer Vision Pattern Recognition, CVPR'83, Washington, DC, June 19-23, 1983, pp. 246-252.
27. B. Furht, A survey of multimedia compression techniques and standards. Part II: Video compression, Real-Time Imaging, vol. 1, pp. 319-337, 1995.
28. L. De Vos and M. Stegherr, Parametrizzable VLSI architectures for the full-search block-matching algorithm, IEEE Trans. Circuits Syst., vol. 36, pp. 1309-1316, Oct. 1989.
29. T. Komarek and P. Pirsch, Array architectures for block matching algorithms, IEEE Trans. Circuits Syst., vol. 36, pp. 1301-1308, Oct. 1989.
30. C.-H. Hsieh and T.-P. Lin, VLSI architecture for block matching motion estimation algorithm, IEEE Trans. Circuits Syst. Video TechnoL, vol. 2, pp. 169-175, June 1992.
31. H. Yeo and Y. H. Hu, A novel modulae systolic array architecture for full-search block matching motion estimation, IEEE Trans. Circuits Syst. Video TechnoL, vol. 5, pp. 407-4116, Oct. 1995.
32. H.-N. Kim, M. J. Irwin, and R. M. Owens, Motion analysis of the micro grained array processor, Special Issue on Real Time Imaging, P. Nesi, Ed. J. Real-Time Syst., vol. 3, no. 2, pp. 101-110, 1997.
33. L64720 motion estimation processor, LSI Logic Corp., Tech. Rep., 1990.
34. -, Data book: SÜ3220 motion estimation processor, SGSThompson, Tech. Rep., 1991.
35. S. Chang, J.-H. Hwang, and C.-W. Jen, Scalable array architecture design for full search block matching, IEEE Trans. Circuits Syst. Video TechnoL, vol. 5, pp. 332-343, Aug. 1995.
36. M.-J. Chen, L.-G. Chen, T.-D. Chiueh, and Y.-P. Lee, A new blockmatching criterion for motion estimation and its implementation, IEEE Trans. Circuits Syst. Video TechnoL, vol. 5, pp. 231-236, June 1995.
37. T. Komarek and P. Pirsch, VLSI architectures for hierarchical block matching algorithm, in Proc. IFIP Workshop, Grenoble, France, Dec. 1989, pp. 168-181.
38. L. De Vos, VLSI-architecures for the hierarchical block-matching algorithm for hdtv applications, SPIE Visual Commun. Image Processing, vol. 1360, pp. 398-409, 1990.
39. B.-M. Wang, J.-C. Yen, and S. Chang, Zero waiting-cycle hierarchical, IEEE Trans. Circuits Syst. Video Technol, vol. 4, pp. 18-28, Feb. 1994.
40. H.-M. Jong, L.-G. Chen, and T.-D. Chiueh, Parallel architecture for 3-step hierarchical search block-matching algorithm, IEEE Trans. Circuits Syst. Video Technol., vol. 4, no. 4, pp. 407-416, 1994.
41. G. Gupta and C. Chakrabarti, Architectures for hierarchical and other block matching algorithms, IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 477-489, Dec. 1995.
42. H. Bulthoff, J. J. Little, and T. Poggio, A parallel algorithm for a realtime computation of optical-flow, Nature, vol. 337, pp. 549-553, Feb. 9, 1989.
43. F. Glazer, G. Reynolds, and P. Anandan, Scene matching by hierarchical correlation, in Proc. IEEE Conf. Computer Vision Pattern Recognition, CVPR'83, Washington, DC, June 19-23, 1983, pp. 432-441.
44. K. M. Nam, J.-S. Kim, R.-H. Park, and Y. S. Shim, A fast hierarchical motion vector estimation algorithm using mean pyramid, IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 344-351, Aug. 1995.
45. A. N. Netravali and J. D. Robbins, Motion-compensated television coding: Part I, Bell Syst. Tech. J., vol. 58, pp. 631-670, Mar. 1979.
46. R. C. Kim and S. U. Lee, A VLSI architecture for a pel recursive motion estimation algorithm, IEEE Trans. Circuits Syst. Video Technol., vol. 36, pp. 1291-1299, Oct. 1989.
47. E. D. Frimout, J. N. Driessen, and E. F. Deprettere, Parallel architecture for a pel-recursive motion estimation algorithm, IEEE Trans. Circuits Syst. Video Technol., vol. 2, pp. 159-168, June 1992.
48. F. Charot, C. Labit, and P. Lemonnier, Architectural study of a blockrecursive motion estimation algorithm, Special Issue on Real Time Imaging, P. Nesi, Ed. J. Real-Time Syst., vol. 3, no. 2, pp. 111-128, Apr. 1997.
49. O. Tretiak and L. Pastor, Velocity estimation from image sequences with second order differential operators, in Proc. 7th IEEE Int. Conf. Pattern Recognition, Montreal, P.Q., Canada, July 30-Aug. 2, 1984, pp. 16-19.
50. A. Verri, F. Girosi, and V. Torre, Differential techniques for optical flow, J. Opt. Soc. Amer. A, vol. 7, pp. 912-922, May 1990.
51. C. Cafforio and F. Rocca, Tracking moving objects in television images, Signal Process., vol. 1, pp. 133-140, 1979.
52. W. Enkelmann, Investigation of multigrid algorithms for the estimation of optical flow fields sequences, Comput. Vis., Graph., Image Processing, vol. 43, pp. 150-177, 1988.
53. M. Tistarelli, Computing optical flow: A real time application of the connection machine system, tr v89-l, Thinking Machines Corp., DIST Genova, Cambridge, MA, Tech. Rep., June 1989.
54. H.-H. Nagel, On a constraint equation for the estimation of displacement rates in image sequences, IEEE Trans. Pattern Anal. Machine Intell, vol. 11, pp. 13-30, Jan. 1989.
55. A. Del Bimbo, P. Nesi, and J. L. C. Sanz, Optical flow estimation by using classical and extended constraints, in Proc. 4th Int. Workshop Time-Varying Image Processing Moving Object Recognition, Florence, Italy, June 10-11, 1993.
56. H.-H. Nagel, On the estimation of optical flow: Relations between different approaches and some new results, Artif. Intell., vol. 33, pp. 299-324, 1987.
57. M. Campani and A. Verri, Computing optical flow from an overconstrained system of linear algebraic equations, in Proc. 3rd IEEE Int. Conf. Computer Vision ICCV'90, Osaka, Japan, Dec. 4-7, 1990, pp. 22-26.
58. B. G. Schunck, Image flow segmentation and estimation by constraints line and clustering, IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 1010-1027, Oct. 1989.
59. D. Ben-Tzvi, A. Del Bimbo, and P. Nesi, Optical flow from constraint lines parametrization, Pattern Recognition, vol. 26, pp. 1549-1561, Nov. 1993.
60. P. Nesi, A. Del Bimbo, and D. Ben-Tzvi, A robust algorithm for optical flow estimation, Comput. Vis., Graph., Image Processing: Image Understanding, vol. 61, pp. 59-68, 1995.
61. A. Del Bimbo, P. Nesi, and J. L. C. Sanz, Innovative multipoint solutions for optical flow estimation with different constraints, in Proc. Int. Conf. Image Anal. Processing, IAPR, Bari, Italy, Sept. 20-23, 1993.
62. P. Nesi, Project OFCOMP: Optical flow for counting moving people, deliverable of task 1.0 (chip RETIMAC specification) of DIM 45 Esprit iii Project MEPI n.7500, rt 25/94, Dip. Sistemi e Informatica, Facolta' di Ingegneria Univ. Studi di Firenze, Florence, Italy, Tech. Rep., Feb. 1994.
63. M. Gottardi, A. Sartori, and A. Simoni, Polifemo: An addressable CMOS 128x 128-pixel image sensor with digital interface; Electrical specifications, IRST, Istituto per la Ricerca Scientifica e Tecnologica, I 38100 TRENTO, Eoc. Pante' di Povo, Tech. Rep., Dec. 1992.