Feature‐fusion based object tracking for robot platforms

Abstract

Purpose Object tracking has been a challenging problem of robot vision over the decades, which plays a key role in a wide spectrum of visual tracking‐related applications such as surveillance, visual servoing, sensing and navigation in robotics, video compression. The purpose of this paper is to present a novel intensity, orientation codes and geometry (IOCG) histogram variant of the mean‐shift algorithm for object tracking. Design/methodology/approach Feature cues of intensity, orientation codes and geometric information are fused together to form an IOCG histogram in combination with a conventional mean‐shift‐based tracking algorithm. Findings Experimental results demonstrate the effectiveness and efficiency of the proposed method. Not only do fusing orientation codes features allow the proposed algorithm to conduct tracking in a cluttered background, but partial occlusion is also solved in the tracker in that spatial information usually lost in a conventional histogram is compensated by the introduced geometric relations between tracked pixels and the center of a tracker template. Originality/value The paper presents a novel vision tracking method for robots.