| Summary: | In this paper we address the problem of tracking features efficiently and robustly along image sequences. To estimate the undergoing movement we use an approach based on Kalman filtering. The measured data is incorporated by optimizing the global correspondence set based on an efficient approximation of the Mahalanobis Distance (MD). Along the image sequence, to deal with the incoming and previously existing features a new management model is considered, so that each occluded feature may be kept on tracking or it may be excluded depending on its historical behavior. This approach handles adequately occlusion, disappearance and (re)appearance of features while tracking efficiently movement in the image scene. It also allows feature tracking in long sequences at low computational cost. Some experimental results are presented.
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