Predicting Ball Ownership in Basketball from a Monocular View Using Only Player Trajectories
Abstract
Tracking objects like a basketball from a monocular view is challenging due to its small size, potential to move at high velocities as well as the high frequency of occlusion. However, humans with a deep knowledge of a game like basketball can predict with high accuracy the location of the ball even without seeing it due to the location and motion of nearby objects, as well as information of where it was last seen. Learning from tracking data is problematic however, due to the high variance in player locations. In this paper, we show that by simply "permuting" the multi-agent data we obtain a compact role-ordered feature which accurately predict the ball owner. We also show that our formulation can incorporate other information sources such as a vision-based ball detector to improve prediction accuracy.
Cite
Text
Wei et al. "Predicting Ball Ownership in Basketball from a Monocular View Using Only Player Trajectories." IEEE/CVF International Conference on Computer Vision Workshops, 2015. doi:10.1109/ICCVW.2015.106Markdown
[Wei et al. "Predicting Ball Ownership in Basketball from a Monocular View Using Only Player Trajectories." IEEE/CVF International Conference on Computer Vision Workshops, 2015.](https://mlanthology.org/iccvw/2015/wei2015iccvw-predicting/) doi:10.1109/ICCVW.2015.106BibTeX
@inproceedings{wei2015iccvw-predicting,
title = {{Predicting Ball Ownership in Basketball from a Monocular View Using Only Player Trajectories}},
author = {Wei, Xinyu and Sha, Long and Lucey, Patrick and Carr, Peter and Sridharan, Sridha and Matthews, Iain A.},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2015},
pages = {780-787},
doi = {10.1109/ICCVW.2015.106},
url = {https://mlanthology.org/iccvw/2015/wei2015iccvw-predicting/}
}