%0 Journal Article %A JI Zhangjian %A REN Xingwang %T Object tracking algorithm of fully-convolutional Siamese networks with rotation and scale estimation %D 2021 %R 10.11772/j.issn.1001-9081.2020111805 %J Journal of Computer Applications %P 2705-2711 %V 41 %N 9 %X In the object tracking task, Fully-Convolutional Siamese networks (SiamFC) tracking method has problems of tracking errors or inaccurate tracking results caused by the rotation and scale variation of objects. Therefore, a SiamFC tracking algorithm with rotation and scale estimation was proposed, which consists of location module and rotation-scale estimation module. Firstly, in the location module, the tracking position was obtained by using SiamFC algorithm, and this position was adjusted by combining the rotation and scale information. Then, in the rotation-scale estimation module, as the image rotation and scale variation were converted into translational motions in log-polar coordinate system, the object search area was transformed from Cartesian coordinate system to log-polar coordinate system, so that the scale and rotation angle of the object were estimated by using correlation filtering technology. Finally, an object tracking model which can simultaneously estimate object position, rotation angle and scale variation was obtained. In the comparison experiments, the proposed algorithm had the success rate and accuracy of 57.7% and 81.4% averagely on Visual Tracker Benchmark 2015 (OTB2015) dataset, and had the success rate and accuracy of 51.8% and 53.3% averagely on Planar Object Tracking in the wild (POT) dataset with object rotation and scale variation. Compared with the success rate and accuracy of SiamFC algorithm, those of the proposed algorithm were increased by 13.5 percentage points and 13.4 percentage points averagely. Experimental results verify that the proposed algorithm can effectively solve the tracking challenges caused by object rotation and scale variation. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020111805