A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis
Abstract
It is well recognised that data association is critically important for object tracking. However, in the presence of successive misdetections, a large number of false candidates and an unknown number of abrupt model switchings that happen unpredictably, the data association problem can be very difficult. We tackle these difficulties by using a layered data association scheme. At the object level, trajectories are "grown" from sets of object candidates that have high probabilities of containing only true positives; by this means the otherwise combinatorial complexity is significantly reduced. Dijkstra's shortest path algorithm is then used to perform data association at the trajectory level. The algorithm is applied to low-quality tennis video sequences to track a tennis ball. Experiments show that the algorithm is robust to abrupt model switchings, and performs well in heavily cluttered environments.
Cite
Text
Yan et al. "A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.36Markdown
[Yan et al. "A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/yan2006cvpr-novel/) doi:10.1109/CVPR.2006.36BibTeX
@inproceedings{yan2006cvpr-novel,
title = {{A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis}},
author = {Yan, Fei and Kostin, Alexey and Christmas, William J. and Kittler, Josef},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {634-641},
doi = {10.1109/CVPR.2006.36},
url = {https://mlanthology.org/cvpr/2006/yan2006cvpr-novel/}
}