Adaptive Learning Rate for Visual Tracking Using Correlation Filters

Abstract

Visual tracking is a difficult problem in computer vision due to illumination, pose, scale, appearance variations of object. Most of the trackers use either gray scale/color information or gradient information for image description. However the use of multiple channel features provide more information than single feature alone. Recently correlation filter based video tracking gained popularity due to its efficiency and high frame rate. Existing correlation filters use fixed learning rate to update filter template in every frame. In this paper, a method for adapting learning rate in correlation filter (CF) is presented which depends on the position of target in the present and previous frames (target velocity). This method uses integral channel features in correlation filter framework with adaptive learning rate to efficiently track the object. We experiment this technique on 12 challenging video sequences from visual object tracking (VOT challenges) datasets. Proposed technique can track any object irrespective of illumination variance, occlusion, scale change and outperforms the state-of-the-art trackers.