Mean shift-based object tracking with multiple features

This paper presents visual features for tracking of moving object in video sequences using Mean Shift algorithm. The features used in this paper are color, edge and texture. Mean shift Algorithm is expanded based on mentioned multiple features, which are described with highly nonlinear models. In the proposed method, firstly all the features is extracted from first frame and the histogram of each feature is computed then the mean shift algorithm is run for each feature independently and the output of the mean shift algorithm for each feature is weighted based on the similarity measure. In last step, center of the target in the new frame is computed through the integration of the outputs of mean shift. We show that tracking with multiple weighted features provides more reliable performance than single features tracking.