A More Robust Mean Shift Tracker Using Joint Monogenic Signal Analysis and Color Histogram

This paper presents a robust object tracking method based on the methodologies of statistical texture analysis of 2D images based on the theory of monogenic signal analysis, jointed with the color histogram. This novel feature extraction method is embedded thereafter in the mean shift framework. Compared with methods of state-of-the-art mean shift trackers, this method proves to be more discriminant and less sensitive to noise. The experimental results proved that our proposed method can achieve robust tracking performances in complex situations with fewer mean shift iterations.