Fast Visual Object Tracking Using Ellipse Fitting for Rotated Bounding Boxes

ICCVW 2019 pp. 2281-2289

doi:10.1109/ICCVW.2019.00281 /iccvw/2019/chen2019iccvw-fast/

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.

ICCVW Semantic Scholar

Cite

Text

Chen and Tsotsos. "Fast Visual Object Tracking Using Ellipse Fitting for Rotated Bounding Boxes." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00281

Markdown

[Chen and Tsotsos. "Fast Visual Object Tracking Using Ellipse Fitting for Rotated Bounding Boxes." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/chen2019iccvw-fast/) doi:10.1109/ICCVW.2019.00281

BibTeX

@inproceedings{chen2019iccvw-fast,
  title     = {{Fast Visual Object Tracking Using Ellipse Fitting for Rotated Bounding Boxes}},
  author    = {Chen, Bao Xin and Tsotsos, John K.},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {2281-2289},
  doi       = {10.1109/ICCVW.2019.00281},
  url       = {https://mlanthology.org/iccvw/2019/chen2019iccvw-fast/}
}