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

Abstract

SUMMARYA high speed target detection and tracking algorithm for a CNN-UM chip is presented in this paper. The target conÿdence 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 conÿdence value and target initiation is done through the temporal accumulation of the conÿdence. The probability of the target existence for each feature is created in the region of in uence 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 targettracking algorithm have been done and properties of the proposed approach are disclosed through the various experiments.

Analogic CNN algorithm for estimating position and size of moving objects

“…E cient separation of moving objects from the background is obtained through automatic threshold selection. The performance of the proposed method is shown using real-life indoor and outdoor video sequences.Moving object detection by CNNs have been investigated in the past, also from the viewpoint of image coding for low bit-rate transmission [6][7][8][9][10]. In this paper, we propose a simple and robust algorithm for moving object detection in video sequences taken from a ÿxed camera.…”