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Tracking Features in Image Sequences with Kalman Filtering, Global Optimization, Mahalanobis Distance and a Management Model

Raquel R. Pinho1, João Manuel R. S. Tavares1
Instituto de Engenharia Mecanica e Gestao Industrial (INEGI), Lab. de Optica e Mecanica Experimental (LOME) / Faculdade de Engenharia da Universidade do Porto (FEUP), Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal, Email: {rpinho, tavares}@fe.up.pt

Computer Modeling in Engineering & Sciences 2009, 46(1), 51-76. https://doi.org/10.3970/cmes.2009.046.051

Abstract

This work addresses the problem of tracking feature points along image sequences. In order to analyze the undergoing movement, an approach based on the Kalman filtering technique has been used, which basically carries out the estimation and correction of the features' movement in every image frame. So as to integrate the measurements obtained from each image into the Kalman filter, a data optimization process has been adopted to achieve the best global correspondence set. The proposed criterion minimizes the cost of global matching, which is based on the Mahalanobis distance. A management model is employed to manage the features being tracked. This model adequately deals with problems related to the occlusion of the tracked features, the appearance of new features, as well as optimizing the computational resources used. Experimental results obtained through the use of the proposed tracking framework are presented.

Keywords

Stochastic Filter, Data Association, Motion Correspondence, Optimization, Mahalanobis Distance, Image Analysis, Tracking

Cite This Article

Pinho, R. R., Manuel, J. (2009). Tracking Features in Image Sequences with Kalman Filtering, Global Optimization, Mahalanobis Distance and a Management Model. CMES-Computer Modeling in Engineering & Sciences, 46(1), 51–76.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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