Comparison between Kalman and unscented Kalman filters in tracking applications of computational vision

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Raquel Ramos Pinho (Author) FEUP; João Manuel Ribeiro da Silva Tavares (Author) FEUP

Conference proceedings International

Title: VipIMAGE 2009 - II ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing

Pages: 299-304

VipIMAGE 2009 - II ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing

Porto, Portugal, 14 a 16 de Outubro de 2009

Indexing

ISI Web of Science

Scientific classification

FOS: Engineering and technology > Mechanical engineering

CORDIS: Technological sciences > Engineering > Mechanical engineering

Other information

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6