@inproceedings{ef296d4c3c0c464ead76c30a27b9ed01,
title = "Bayesian tracking of multiple objects with vision and radar",
abstract = "This paper is concerned with a system for detecting and tracking multiple 3D bounding boxes based on information from multiple sensors. Our framework is built around an inference engine similar to the probability hypothesis density (PHD) filter, where the state space consists of stochastic bounding boxes with constant velocity dynamics. We outline measurement equations for two modalities (vision and radar). The result is a flexible inference system suitable for use on autonomous vehicles.",
author = "Michael Hoy and Chaoqun Weng and Junsong Yuan and Justin Dauwels",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016 ; Conference date: 13-11-2016 Through 15-11-2016",
year = "2016",
doi = "10.1109/ICARCV.2016.7838788",
language = "English",
series = "2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016",
address = "United States",
}