Fast Online Object Tracking and Segmentation: A Unifying Approach
Abstract
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task. Once trained, SiamMask solely relies on a single bounding box initialisation and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second. Despite its simplicity, versatility and fast speed, our strategy allows us to establish a new state-of-the-art among real-time trackers on VOT-2018, while at the same time demonstrating competitive performance and the best speed for the semi-supervised video object segmentation task on DAVIS-2016 and DAVIS-2017.
Cite
Text
Wang et al. "Fast Online Object Tracking and Segmentation: A Unifying Approach." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00142Markdown
[Wang et al. "Fast Online Object Tracking and Segmentation: A Unifying Approach." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/wang2019cvpr-fast/) doi:10.1109/CVPR.2019.00142BibTeX
@inproceedings{wang2019cvpr-fast,
title = {{Fast Online Object Tracking and Segmentation: A Unifying Approach}},
author = {Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip H.S.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00142},
url = {https://mlanthology.org/cvpr/2019/wang2019cvpr-fast/}
}