Multi-Object Tracking Using a Generalized Multi-Object First-Order Moment Filter
Abstract
The optimal approach to multisensor, multi-object fusion, detection, tracking, and identification is a suitable generalization of the recursive Bayes filter. Since this filter is computationally intractable in general, the first author has proposed an approximation of it based on propagation of a multi-object first-order moment statistic called the "probability hypothesis density" (PHD). Using more powerful proof techniques, we show that the original assumption of state-independent probability of detection can be removed. We also provide a less restrictive method for fusing multi-sensor data. A particle-systems implementation of the PHD filter is illustrated in a simple "toy" scenario.
Cite
Text
Mahler and Zajic. "Multi-Object Tracking Using a Generalized Multi-Object First-Order Moment Filter." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10098Markdown
[Mahler and Zajic. "Multi-Object Tracking Using a Generalized Multi-Object First-Order Moment Filter." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/mahler2003cvprw-multiobject/) doi:10.1109/CVPRW.2003.10098BibTeX
@inproceedings{mahler2003cvprw-multiobject,
title = {{Multi-Object Tracking Using a Generalized Multi-Object First-Order Moment Filter}},
author = {Mahler, Ronald P. S. and Zajic, Tim},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2003},
pages = {99},
doi = {10.1109/CVPRW.2003.10098},
url = {https://mlanthology.org/cvprw/2003/mahler2003cvprw-multiobject/}
}