Automatic detection and tracking of moving image target with CNN-UM via target probability fusion of multiple features
Article first published online: 2 JUL 2003
Copyright © 2003 John Wiley & Sons, Ltd.
International Journal of Circuit Theory and Applications
Volume 31, Issue 4, pages 329–346, July/August 2003
How to Cite
Kim, H., Roska, T., Chua, L. O. and Werblin, F. (2003), Automatic detection and tracking of moving image target with CNN-UM via target probability fusion of multiple features. Int. J. Circ. Theor. Appl., 31: 329–346. doi: 10.1002/cta.235
- Issue published online: 2 JUL 2003
- Article first published online: 2 JUL 2003
- Manuscript Revised: 30 AUG 2002
- Manuscript Received: 19 DEC 2001
- Office of Naval Research. Grant Number: N00014-99-1-0959 and N00014-00-0295
- target detection;
- multiple features;
- region of influence
A high speed target detection and tracking algorithm for a CNN-UM chip is presented in this paper. The target confidence value is computed based on the fusion of target existence probabilities of features using products of weighted sums. The target decision is done with such a confidence value and target initiation is done through the temporal accumulation of the confidence. The probability of the target existence for each feature is created in the region of influence depending on the reliability and the strength of the feature. By virtue of the analogic parallel processing structure of the CNN-UM (Roska T, Chua LO. The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Systems II 1993; CAS-40: 163–173), real time tracking can be achieved with presently available technologies with the speed of several kilo-frames per second. Due to the utilization of multiple features of target, robust target detection is possible via the proposed algorithm. On-chip experiments of the proposed target-tracking algorithm have been done and properties of the proposed approach are disclosed through the various experiments. Copyright © 2003 John Wiley & Sons, Ltd.