Confidence-Aware Probability Hypothesis Density Filter for Visual Multi-Object Tracking

Nicolai Wojke, Dietrich Paulus

2017

Abstract

The Probability Hypothesis Density Filter (PHD) filter is an efficient recursive multi-object state estimator that systematically deals with data association uncertainty. In this paper, we apply the PHD filter in a tracking-bydetection framework. In order to mimic state-dependent false alarms, we introduce an adapted PHD recursion that defines clutter generators in state space. Further, we integrate detector confidence scores into the measurement likelihood. This extension is quite effective yet simple, which means that it requires few changes to the original PHD recursion, that it has the same computational complexity, and that there exist few parameters that must be adapted to the individual tracking scenario. Our evaluation on a popular pedestrian tracking dataset demonstrates results that are competitive with the state-of-the-art.

Paper Citation

in Harvard Style

Wojke N. and Paulus D. (2017). Confidence-Aware Probability Hypothesis Density Filter for Visual Multi-Object Tracking . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 132-139. DOI: 10.5220/0006095801320139

in Bibtex Style

@conference{visapp17,
author={Nicolai Wojke and Dietrich Paulus},
title={Confidence-Aware Probability Hypothesis Density Filter for Visual Multi-Object Tracking},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={132-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006095801320139},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Confidence-Aware Probability Hypothesis Density Filter for Visual Multi-Object Tracking
SN - 978-989-758-227-1
AU - Wojke N.
AU - Paulus D.
PY - 2017
SP - 132
EP - 139
DO - 10.5220/0006095801320139