Target Tracking in an Image Sequence Using Wavelet Features and a Neural Network

In this paper, we present a comprehensive study to design an artificial neural network (ANN) for tracking a target in an image sequence. The proposed ANN architecture is a single-hidden-layer back-propagation neural network (BPNN), in which the sigmoid and the linear activation functions are used for its hidden and output layers, respectively. The features used for the input layer of the BPNN are 4
^th
level Daubechies's wavelet decomposition coefficients corresponding to the input image. Performances of dbl, db2, db3, and db4 wavelet features are compared. The object, which is tracked for the purpose of demonstration, is a specific airplane. However, the proposed ANN model can be trained to track any other object of interest. The trained ANN has been simulated and tested on the training and testing datasets. The tracking error is analyzed with post-regression analysis tool, which finds the correlation among the calculated coordinates and the correct coordinates of the object in the image. The promising results of the presented computer simulation and analysis show that the proposed target tracking technique exploiting the powers of ANN and wavelet transform is quite plausible and significantly robust.