Robust Gaussian-based template tracking

Abstract

In this paper a visual object tracking method is presented which is robust against changes in the object appearance, shape, and scale. This method is also able to track objects being occluded temporarily in cluttered environments. It is assumed the target object moves freely through an unpredicted pattern in a dynamic environment where the camera may not be stationary. The proposed method models the object representation by an adaptive and deformable template which consists of several Gaussian functions. A 5 degree-of-freedom transformation function is employed to map the pixels from the template reference frame to the image reference frame. Moreover, the object localization method is based on a robust probabilistic optimization algorithm which is performed at every image frame to estimate the transformation parameters. The comparisons of the results obtained by the proposed tracker and several state-of-the-art methods with the manually labeled ground truth data demonstrate higher accuracy and robustness of the proposed method in this work. © Springer-Verlag Berlin Heidelberg 2012.