Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes

Bao Xin Chen, John Tsotsos; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Abstract


In this paper, we demonstrate a novel algorithm that uses ellipse fitting to estimate 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-art object 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). We tested our approach on the visual object tracking datasets (VOT2016, VOT2018, and VOT2019) that were labeled with rotated bounding boxes. By comparing with the original SiamMask, we achieved an improved Accuracy of 64.5% and 30.3% EAO on VOT2019, which is 4.9% and 2% higher than the original SiamMask. The implementation is available on GitHub: https://github.com/baoxinchen/siammask_e.

Related Material


[pdf]
[bibtex]
@InProceedings{Chen_2019_ICCV,
author = {Xin Chen, Bao and Tsotsos, John},
title = {Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}