Joint Probabilistic Data Association Revisited
Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid
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.
BibTeX
@inproceedings{iccv2015_jointprobabilist,
title = {Joint Probabilistic Data Association Revisited},
author = {Seyed Hamid Rezatofighi and Anton Milan and Zhen Zhang and Qinfeng Shi and Anthony Dick and Ian Reid},
booktitle = {ICCV 2015},
year = {2015}
}