RETIMAC: REal-TIme Motion Analysis Chip

Motion estimation is relevant for applications of both motion-compensated image sequence processing and dynamic scene analysis of computer vision. Different approaches and solutions have been proposed for these two applicative fields. In some cases, parallel architectures and dedicated chips for motion estimation in real-time have been developed. In this paper, a low-cost REal-TIme Motion Analysis Chip, RETIMAC, is presented, which is suitable for dynamic scene analysis in computer vision applications. This chip is capable of estimating optical flow fields in real-time, and has been especially developed for project OFCOMP (Optical Flow for COunting Moving People) DTM 45 ESPRIT III MEPI. It can be profitably used also for autonomous navigation, tracking, surveillance, counting moving objects, measuring velocity, etc., and for several computer vision applications which require as a first processing step the estimation of the apparent velocity of each pixel in the image plane (e.g., optical flow, velocity field). RETIMAC implements a gradient-based solution which has been demonstrated to be more reliable and precise with respect to several solutions proposed in the literature.

[1]  Peter Pirsch,et al.  VLSI architectures for hierarchical block matching algorithms , 1990, IEEE International Symposium on Circuits and Systems.

[2]  Chein-Wei Jen,et al.  Scalable array architecture design for full search block matching , 1995, IEEE Trans. Circuits Syst. Video Technol..

[3]  Frederic Dufaux,et al.  Motion estimation techniques for digital TV: a review and a new contribution , 1995, Proc. IEEE.

[4]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[5]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..

[6]  Alessandro Verri,et al.  Computing optical flow from an overconstrained system of linear algebraic equations , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[7]  Alberto Del Bimbo,et al.  Analysis of optical flow constraints , 1995, IEEE Trans. Image Process..

[8]  HANS-HELLMUT NAGEL,et al.  On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Alberto Del Bimbo,et al.  Optical flow from constraint lines parametrization , 1993, Pattern Recognit..

[10]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[11]  François Charot,et al.  Architectural Study of a Block-Recursive Motion Estimation Algorithm , 1997, Real Time Imaging.

[12]  Jake K. Aggarwal,et al.  On the computation of motion from sequences of images-A review , 1988, Proc. IEEE.

[13]  Christer Svensson,et al.  Single-Chip High-Speed Computation of Optical Flow , 1990, MVA.

[14]  Young Serk Shim,et al.  A fast hierarchical motion vector estimation algorithm using mean pyramid , 1995, IEEE Trans. Circuits Syst. Video Technol..

[15]  T. Poggio,et al.  A parallel algorithm for real-time computation of optical flow , 1989, Nature.

[16]  Ajit Singh,et al.  Optic flow computation : a unified perspective , 1991 .

[17]  Liang-Gee Chen,et al.  Parallel architectures for 3-step hierarchical search block-matching algorithm , 1994, IEEE Trans. Circuits Syst. Video Technol..

[18]  Brian G. Schunck,et al.  Image Flow Segmentation and Estimation by Constraint Line Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  F. Jutand,et al.  A versatile and powerful chip for real-time motion estimation , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[20]  D J Heeger,et al.  Model for the extraction of image flow. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[21]  J. D. Robbins,et al.  Motion-compensated television coding: Part I , 1979, The Bell System Technical Journal.

[22]  Mary Jane Irwin,et al.  Motion Analysis on the Micro Grained Array Processor , 1997, Real Time Imaging.

[23]  Borko Furht A Survey of Multimedia Compression Techniques and Standards. Part II: Video Compression , 1995, Real Time Imaging.

[24]  A J Ahumada,et al.  Model of human visual-motion sensing. , 1985, Journal of the Optical Society of America. A, Optics and image science.

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

[26]  Paolo Nesi,et al.  A Vision System for Estimating People Flow , 1996 .

[27]  Michael Stegherr,et al.  Parameterizable VLSI architectures for the full-search block-matching algorithm , 1989 .

[28]  Shyang Chang,et al.  Zero waiting-cycle hierarchical block matching algorithm and its array architectures , 1994, IEEE Trans. Circuits Syst. Video Technol..

[29]  Alberto Del Bimbo,et al.  A Robust Algorithm for Optical Flow Estimation , 1995, Comput. Vis. Image Underst..

[30]  C. Cafforio,et al.  Tracking moving objects in television images , 1979 .

[31]  Liang-Gee Chen,et al.  A new block-matching criterion for motion estimation and its implementation , 1995, IEEE Trans. Circuits Syst. Video Technol..

[32]  Tomaso A. Poggio,et al.  Motion Field and Optical Flow: Qualitative Properties , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  A. Del Bimbo,et al.  Optical flow estimation on Connection-Machine 2 , 1993, 1993 Computer Architectures for Machine Perception.

[34]  Chaitali Chakrabarti,et al.  Architectures for hierarchical and other block matching algorithms , 1995, IEEE Trans. Circuits Syst. Video Technol..

[35]  P. Pirsch,et al.  Advances in picture coding , 1985, Proceedings of the IEEE.

[36]  Yu Hen Hu,et al.  A novel modular systolic array architecture for full-search block matching motion estimation , 1995, IEEE Trans. Circuits Syst. Video Technol..

[37]  Paolo Nesi,et al.  Variational approach to optical flow estimation managing discontinuities , 1993, Image Vis. Comput..

[38]  A. Verri,et al.  Differential techniques for optical flow , 1990 .

[39]  J. van Santen,et al.  Temporal covariance model of human motion perception. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[40]  Ming-Ting Sun,et al.  A family of vlsi designs for the motion compensation block-matching algorithm , 1989 .

[41]  Matthias Schöbinger,et al.  VLSI architecture for a flexible block matching processor , 1995, IEEE Trans. Circuits Syst. Video Technol..

[42]  P. Nesi,et al.  Optical Flow Estimation by Using Classical and Extended Constraints , 1994 .

[43]  Jorge L. C. Sanz,et al.  Optical flow computation using extended constraints , 1996, IEEE Trans. Image Process..

[44]  Harry Wechsler,et al.  Derivation of optical flow using a spatiotemporal-Frequency approach , 1987, Comput. Vis. Graph. Image Process..

[45]  F. Glazer,et al.  Scene Matching by Hierarchical Correlation , 1983 .

[46]  Ed F. Deprettere,et al.  Parallel architecture for a pel-recursive motion estimation algorithm , 1992, CompEuro 1992 Proceedings Computer Systems and Software Engineering.

[47]  Paolo Nesi,et al.  A Robust Tracking of 3D Motion , 1994, ECCV.

[48]  Peter Pirsch,et al.  Array architectures for block matching algorithms , 1989 .

[49]  Chaur-Heh Hsieh,et al.  VLSI architecture for block-matching motion estimation algorithm , 1992, IEEE Trans. Circuits Syst. Video Technol..

[50]  Rin Chul Kim,et al.  A VLSI architecture for a pel recursive motion estimation algorithm , 1989 .

[51]  Wilfried Enkelmann,et al.  Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences , 1988, Comput. Vis. Graph. Image Process..