Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
Abstract
Standard RGB-D trackers treat the target as a 2D structure, which makes modelling appearance changes related even to out-of-plane rotation challenging. This limitation is addressed by the proposed long-term RGB-D tracker called OTR - Object Tracking by Reconstruction. OTR performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance- enhancing features: (i) generation of an accurate spatial support for constrained DCF learning from its 2D projection and (ii) point-cloud based estimation of 3D pose change for selection and storage of view-specific DCFs which robustly localize the target after out-of-view rotation or heavy occlusion. Extensive evaluation on the Princeton RGB-D tracking and STC Benchmarks shows OTR outperforms the state-of-the-art by a large margin.
Cite
Text
Kart et al. "Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00143Markdown
[Kart et al. "Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/kart2019cvpr-object/) doi:10.1109/CVPR.2019.00143BibTeX
@inproceedings{kart2019cvpr-object,
title = {{Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters}},
author = {Kart, Ugur and Lukezic, Alan and Kristan, Matej and Kamarainen, Joni-Kristian and Matas, Jiri},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00143},
url = {https://mlanthology.org/cvpr/2019/kart2019cvpr-object/}
}