Joint Probabilistic Data Association Revisited
Abstract
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.
Cite
Text
Rezatofighi et al. "Joint Probabilistic Data Association Revisited." International Conference on Computer Vision, 2015. doi:10.1109/ICCV.2015.349Markdown
[Rezatofighi et al. "Joint Probabilistic Data Association Revisited." International Conference on Computer Vision, 2015.](https://mlanthology.org/iccv/2015/rezatofighi2015iccv-joint/) doi:10.1109/ICCV.2015.349BibTeX
@inproceedings{rezatofighi2015iccv-joint,
title = {{Joint Probabilistic Data Association Revisited}},
author = {Rezatofighi, Seyed Hamid and Milan, Anton and Zhang, Zhen and Shi, Qinfeng and Dick, Anthony and Reid, Ian},
booktitle = {International Conference on Computer Vision},
year = {2015},
doi = {10.1109/ICCV.2015.349},
url = {https://mlanthology.org/iccv/2015/rezatofighi2015iccv-joint/}
}