Abstract
A target tracking algorithm with the region-based confidence computation on the CNN-UM is proposed. The CNN-UM is an analog parallel computational system which handles regions easily with its region creating capability, parallel processing in the region and regional constraining capability. If the probability for each feature is created in each region, the total confidence of a target can be computed with a fusion algorithm employing products of weighted sums of feature probabilities. The cell-wise target decision in the region can be performed depending on the confidence value at each cell. By virtue of the analog parallel computational structure of the CNN-UM, the computation speed is very fast. On chip experimental results are included in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chua, L. O., Yang, L.: Cellular neural networks: theory. IEEE Tr. on Circuits Systems, vol.35, pp. 1257–1272, 1988.
Chua, L. O., Yang, L.: Cellular neural networks: applications. IEEE Tr. on Circuits Systems, vol. 35, pp. 1273–1290, 1988.
Roska, T., Chua, L. O.: The CNN universal machine: an analogic array computer. IEEE Tr. on Circuits Systems II, CAS-40, pp. 163–173, 1988.
Rekeczky, C., Tahy, A., Vegh, Z., Roska, T.:CNN-base spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography. International Journal of Circuit Theory and Applications, vol. 27, pp. 171–207, 1999.
Czuni, L., Sziranyi, T.: Motion segmentation and tracking with edge relaxation and optimization using fully parallel methods in the Cellular Nonlinear Network Architecture. Real-Time Imaging, vol.7, pp.77–95, 2001.
Analogical and Neural Computing Lab. SimCNN-Multi-layer CNN Simulator for Visual Mouse Platform: Reference Manual version 2.2, Computer and Automation Institute (MTA SzTAKI) of the Hungarian Academy of Sciences, 1998.
Espejo, S., Domínguez-Castro, R., Liñán, G., Rodríguez-Vázquez, A.: A 64x64 CNN universal chip with analog and digital I/O. Proc. 5th Int. Conf. on Electronics, Circuits and Systems (ICECS-98), Lisbon, Portugal, pp. 203–206 1998.
Roska, T., Zarándy, A., Zäld, S., Fäldesy P., Szolgay, P.:The computational infrastructure of analogic CNN computing-Part I: The CNN-UM chip prototyping system. IEEE Trans. on Circuits and Systems I: Special Issue on Bio-Inspired Processors and Cellular Neural Networks for Vision, (CAS-I Special Issue), Vol. 46, No.2, pp. 261–268, 1999
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, Hs., Son, Hr., Lim, Yj., Chung, Jc. (2002). Target Tracking via Region-Based Confidence Computation with the CNN-UM. In: Chen, YC., Chang, LW., Hsu, CT. (eds) Advances in Multimedia Information Processing — PCM 2002. PCM 2002. Lecture Notes in Computer Science, vol 2532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36228-2_96
Download citation
DOI: https://doi.org/10.1007/3-540-36228-2_96
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00262-8
Online ISBN: 978-3-540-36228-9
eBook Packages: Springer Book Archive