Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/43605
Author(s): Raquel Ramos Pinho
João Manuel Ribeiro da Silva Tavares
Title: Comparison between Kalman and unscented Kalman filters in tracking applications of computational vision
Issue Date: 2009
Abstract: In this paper, the problem of tracking feature points along image sequences is addressed. The establishment of correspondences between points and their tracking along image sequences is a complex problem in Computational Vision; especially, when intricate motions, erroneously detections or cases of occlusion or appearance/disappearing of features are involved. To overcome some of those difficulties, a statistical ap-proach is frequently used in a multi-object data association and state estimation framework. Additionally, the correspondence between each measurement and predicted feature can be performed by minimizing the overall Mahalanobis distance. Under these circumstances, the estimation of the system can be accomplished using different stochastic filters. Hereby, a comparison is made between the results obtained, with the described framework, either by the Kalman Filter or the Unscented Kalman Filter, in the tracking of linear and non-linear motions of feature points along image sequences.
Subject: Engenharia mecânica, Engenharia mecânica
Mechanical engineering, Mechanical engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia mecânica
Engineering and technology::Mechanical engineering
URI: https://repositorio-aberto.up.pt/handle/10216/43605
Source: VipIMAGE 2009 - II ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

Files in This Item:
File Description SizeFormat 
57521.pdf185.43 kBAdobe PDFThumbnail
View/Open


This item is licensed under a Creative Commons License Creative Commons