Automatic detection and tracking of moving image target with CNN-UM via target probability fusion of multiple features

Hyongsuk Kim, Támas Roska, Leon O. Chua, Frank Werblin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)329-346
Number of pages18
JournalInternational Journal of Circuit Theory and Applications
Volume31
Issue number4
DOIs
Publication statusPublished - Jul 1 2003

Keywords

  • Confidence
  • Fusion
  • Multiple features
  • Region of influence
  • Target detection

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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