Real-time target object tracking via dual-directional scaling state predicting

Visual tracking using correlation filters can achieve a high running speed. However, it is still a challenging problem to deal with drastic appearance changes (e.g. large and rapid scale variation of a target) during tracking. In this Letter, a novel dual-directional scaling state predicting method based on correlation filtering is proposed. The adaptive horizontal and vertical searches are adopted to separately and accurately estimate the scale changes in different directions. The online scale to size module is designed to enhance the prediction robustness of the target appearance. Experimental comparisons with several state-of-the-art object trackers show the effectiveness and efficiency of the proposed approach.