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In this paper, we demonstrate a novel algorithm that uses ellipse fitting toestimate the bounding box rotation angle and size with the segmentation(mask)on the target for online and real-time visual object tracking. Our method,SiamMask_E, improves the bounding box fitting procedure of the state-of-the-artobject tracking algorithm SiamMask and still retains a fast-tracking frame rate(80 fps) on a system equipped with GPU (GeForce GTX 1080 Ti or higher). Wetested our approach on the visual object tracking datasets (VOT2016, VOT2018,and VOT2019) that were labeled with rotated bounding boxes. By comparing withthe original SiamMask, we achieved an improved Accuracy of 0.652 and 0.309 EAOon VOT2019, which is 0.056 and 0.026 higher than the original SiamMask. Theimplementation is available on GitHub:https://github.com/baoxinchen/siammask_e.

Fast Visual Object Tracking with Rotated Bounding Boxes