Object tracking using the Gabor wavelet transform and the golden section algorithm

This paper presents a new method for tracking an object in a video sequence which uses a 2D Gabor wavelet transform (GWT), a 2D mesh, and a 2D golden section algorithm. An object is modeled by local features from a number of the selected feature points, and the global placement of these feature points. The feature points are stochastically selected based on the energy of their GWT coefficients. Points with higher energy have a higher probability of being selected. The amplitudes of the GWT coefficients of a feature point are then used as the local feature. The global placement of the feature points is determined by a 2D mesh whose feature is the area of the triangles formed by the feature points. The overall similarity between two objects is a weighted sum of the local and global similarities. In order to find the corresponding object in the video sequence, the 2D golden section algorithm is employed, and this can be shown to be the fastest algorithm to find the maximum of a unimodal function. Our results show that the method is robust to object deformation and supports object tracking in noisy video sequences.