Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression
Abstract
A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.
Cite
Text
Kim et al. "Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00036Markdown
[Kim et al. "Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/kim2019iccv-instancelevel/) doi:10.1109/ICCV.2019.00036BibTeX
@inproceedings{kim2019iccv-instancelevel,
title = {{Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression}},
author = {Kim, Kyung-Rae and Choi, Whan and Koh, Yeong Jun and Jeong, Seong-Gyun and Kim, Chang-Su},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00036},
url = {https://mlanthology.org/iccv/2019/kim2019iccv-instancelevel/}
}