@inproceedings{81286fcb8ba043768b8cfbe5866a7cc4,
title = "Multitarget PHD Particle Filter Tracker Based on Single-Target PHD",
abstract = "Probability hypothesis density (PHD) filter is a new practical method for tracking multiple targets. However, to obtain the output of the PHD filter, peaks-extraction and association between frames is needed to estimate the target states and the individual target trajectories. A new PHD filter tracker based on single-target PHD is proposed. The method estimates target states by decomposing PHD into single-target PHDs, and associates target location estimates between time frames based on the additional labels of particles. Simulation results demonstrate that the new algorithm provides more accurate trajectory estimations and is more efficient than the PHD tracker with k-means algorithm and the usual particle-labeling association.",
keywords = "Multitarget tracking, PHD particle filter, State estimation, Track continuity",
author = "Lingling Zhao and Xiaohong Su and Peijun Ma",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media, LLC 2012.; International Conference in Electrics, Communication and Automatic Control, ECAC 2011 ; Conference date: 23-06-2011 Through 24-06-2011",
year = "2012",
doi = "10.1007/978-1-4419-8849-2\_221",
language = "英语",
isbn = "9781441988485",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1705--1712",
editor = "Ran Chen",
booktitle = "2011 International Conference in Electrics, Communication and Automatic Control Proceedings",
address = "德国",
}