Abstract
In this paper we present a method for 2D temporal tracking of region in a monocular sequence of images. The approach we propose starts with a temporal matching phase which consists of forecasting the future system state. Kalman filtering is used during this phase. The goal here is to estimate, with a given reliability rate, the position and the size of the region in the next image in which the potential corresponding principal axis can be found. This phase is only applied to the principal inertia axes of each region. The following phase is the spatial matching which consists of finding the most probable matching among the principal inertia axes present in the search area. A similarity function is then used. Namely, the Mahalanobis distance, which we apply to descriptors of these principal axes. We then propagate this matching to regions. The same similitude function is used, but is now applied on regions descriptors.
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