An improved management model for tracking multiple features in long image sequences

Abstract

In this paper we present a management model to deal with the problem of tracking a large number of features during long image sequences. Some usual difficulties are related to this problem: features may be temporarily occluded or might even have disappeared definitively; the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This methodology may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.