eprintid: 1931
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/19/31
datestamp: 2023-11-09 15:50:06
lastmod: 2023-11-09 15:50:06
status_changed: 2023-11-09 15:41:39
type: conference_item
metadata_visibility: show
creators_name: Salih, Y.
creators_name: Malik, A.S.
title: 3D object tracking using three Kalman filters
ispublished: pub
keywords: 3D object tracking; 3D tracking; EKF; Gaussian probability distributions; Linear estimators; Linear Kalman filters; LKF; System's dynamics; Tracking algorithm; Tracking application; UKF; Unscented Kalman Filter; Visual Tracking, Information science; Probability distributions; Three dimensional; Tracking (position), Kalman filters
note: cited By 5; Conference of 2011 IEEE Symposium on Computers and Informatics, ISCI 2011 ; Conference Date: 20 March 2011 Through 22 March 2011; Conference Code:86204
abstract: In the recent years, 3D tracking has gained attention due to the perforation of powerful computers and the increasing interest in tracking applications. One of the most common tracking algorithms used is the Kalman filter. Kalman filter is a linear estimator that is based on approximating system's dynamics using Gaussian probability distribution. In this paper, we provide a detailed evaluation of the most common Kalman filters, their use in the literature and their implementation for 3D visual tracking. The main types of Kalman filters discussed are linear Kalman filter, extended Kalman filer and unscented Kalman filter. © 2011 IEEE.
date: 2011
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052135233&doi=10.1109%2fISCI.2011.5958966&partnerID=40&md5=b1d8077df8f480f47948356cd9e39a75
id_number: 10.1109/ISCI.2011.5958966
full_text_status: none
publication: ISCI 2011 - 2011 IEEE Symposium on Computers and Informatics
place_of_pub: Kuala Lumpur
pagerange: 501-505
refereed: TRUE
isbn: 9781612846903
citation:   Salih, Y. and Malik, A.S.  (2011) 3D object tracking using three Kalman filters.  In: UNSPECIFIED.