IMPLEMENTATION OF A PATH DETECTION ALGORITHM FOR MOVING IMAGES ON THE CNN UNIVERSAL MACHINE

Abstract

CNN Universal Machines that contain two different processors working interactively with each other, have an important impact for image processing applications with their advanced computing features. These processors are named as ACE16k processor which is the hardware implementation of cellular neural networks and Digital Signal Processor (DSP). Bio-inspired (Bi-i) Cellular Vision System is also a CNN Universal Machine and its standalone architecture is built on CNN-type (ACE16k) and DSP-type microprocessors. In this study, certain objects in moving images are detected and their features are extracted. By using these features, an algorithm that finds out the path of moving objects is implemented on the Bi-i Cellular Vision System. Finally, the output images obtained as a result of this implementation are evaluated. Keywords: CNN Universal Machine, Bi-i Cellular Vision System, ACE16k, Digital Signal Processor, Target Tracking.

Keywords

• CNN Universal Machine, Bi-i Cellular Vision System, ACE16k, Digital Signal Processor, Target Tracking

References

1. C. Gonzales and R.E. Woods, “Digital Image Processing”, Prentice Hall, New Jersey, 2002.
2. T. Acharya and A.K. Ray, “Image Processing: Principles and Applications”, Wiley and Sons, 2005.
3. L. O. Chua and L. Yang, ” Cellular neural networks: Theory and applications”, IEEE Trans. on CAS, vol. 35 no. 10, pp.1257–1290, 1988.
4. T. Roska and L. O. Chua, “The CNN universal machine: an analogic array computer”, IEEE Trans. on CAS-I, vol.. 40 no.3, pp. 163–173, 1993.
5. T. Roska and A. Rodriguez-Vazquez, “Towards visual microprocessors”, Proceedings of the IEEE, vol. 90 no.7, pp. 1244–1257, 2002.
6. A.R. Vazquez, G. L. Cembrano, L. Carranza, E.R.Moreno, R.C. Galan, F.J. Garrido , R.D. Castro and S. E. Meana, “ACE16k: the third generation of mixed-signal SIMDCNN ACE chips toward VSoCs”, IEEE Trans. CAS-I, vol. 51,no.5, pp. 851–863, 2004.
7. http://www.analogic-computers.com /Support
8. A. Zarandy and C. Rekeczky, “Bi-i: a standalone ultra high speed cellular vision system.”, IEEE Circuit and Systems Magazine, vol. 5, no.2, pp. 36–45, 2005.

9. CUCCHIARA, R., GRANA, C., NERI, G., PICCARDI, M. , and PRATI, A., The Sakbot System for Moving Object Detection and Tracking, Video-Based Surveillance Systems- Computer Vision and Distributed Processing, pp.145-157, 2001.
10. ROTA, N., and THONNAT, M., Video sequence interpretation for visual surveillance, Proc. of Third IEEE International Workshop on Visual Surveillance, pp. 59-68, 2000.
11. JUNG, B. and SUKHATME, G.S., Detecting Moving Objects using a Single Camera on a Mobile Robot in an Outdoor Environment, In the 8th Conference on Intelligent Autonomous Systems, pp. 980-987, Amsterdam, The Netherlands, March 10-13, 2004.
12. BEHRAD, A., SHAHROKNI, A. and MOTAMEDI, S.A., A robust vision-based moving target detection and tracking system. In the Proceeding of Image and Vision Computing Conference, University of Otago, Dunedin, New Zealand, November, 2001.
13. YILMAZ, A., JAVED, O., and SHAH, M., Object tracking: A survey. ACM Comput. Surv. 38(4), Article 13, 45 pages, 2006.