Frechet Distance for Uncertain Curves

In this article, we study a wide range of variants for computing the (discrete and continuous) Frechet distance between uncertain curves. An uncertain curve is a sequence of uncertainty regions, where each region is a disk, a line segment, or a set of points. A realisation of a curve is a polyline connecting one point from each region. Given an uncertain curve and a second (certain or uncertain) curve, we seek to compute the lower and upper bound Frechet distance, which are the minimum and maximum Frechet distance for any realisations of the curves. We prove that both problems are NP-hard for the Frechet distance in several uncertainty models, and that the upper bound problem remains hard for the discrete Frechet distance. In contrast, the lower bound (discrete [5] and continuous) Frechet distance can be computed in polynomial time in some models. Furthermore, we show that computing the expected (discrete and continuous) Frechet distance is #P-hard in some models. On the positive side, we present an FPTAS in constant dimension for the lower bound problem when Delta/delta is polynomially bounded, where delta is the Frechet distance and.bounds the diameter of the regions. We also show a near-linear-time 3-approximation for the decision problem on roughly delta-separated convex regions. Finally, we study the setting with Sakoe-Chiba time bands, where we restrict the alignment between the curves, and give polynomial-time algorithms for the upper bound and expected discrete and continuous Frechet distance for uncertainty modelled as point sets.