AN EFFECTIVE HIERARCHICAL RESOLUTION LEARNING METHOD FOR LOW-RESOLUTION TARGETS TRACKING

Suffering from the low-resolution target's visual quality, the precisions of visual object trackers are reduced. This paper proposes an effective hierarchical resolution learning method for low-resolution targets tracking, abbreviated as HRT. We adopt a hierarchical structure to exploit information from different resolution levels. (1) At the high level: the super-resolution (SR) images, determining the target’s shape, contains richer image textures and clearer target contours, and transmits the search region to the low level. (2) At the low level: low-resolution (LR) images maintain the spatial structure information of the original target, providing the precise center coordinates of the target. Experimental results demonstrate the effectiveness of the proposed tracker, which HRT achieves 90.3% precision on OTB100 LR sequences and 78.5% precision on LR sequences from UAV123 datasets, gaining 2.0%, 2.4% improvement over state-of-the-art trackers respectively.