Object tracking using color correlogram

Color histogram based representations have been widely used for blob tracking. In this paper, a new color histogram based approach for object representation is proposed. By using a simplified version of color correlogram as object feature, spatial information is incorporated into object representation, which allows variations of rotation to be detected throughout the tracking therefore rotational objects can be more accurately tracked. The gradient decent method mean shift algorithm is adopted as the central computational module and further extended to a 3D domain to find the most probable target position and orientation simultaneously. The capability of the tracker to tolerate appearance changes like orientation changes, small scale changes, partial occlusions and background scene changes is demonstrated using real image sequences.