Fusing image, GPS and GIS for road tracking using multiple condensation particle filters

In this paper we present a multiple particle filter framework for accurate real road tracking application, which fuses image features, GPS and map data. We represent the road as a set of connected arcs extracted from a digital map. The state space for the tracker not only contains global variables such as GPS coordinates, but also local variables such as road are parameters. Moreover, the dimension of the state space varies with the number of road arcs. A multiple particle filter framework is developed in order to: 1) improve sampling efficiency for a large state space; 2) cope with the variable dimension of the state space; 3) integrate image-based road tracking (local) and GPS (global) data. Each tracker applies the condensation filtering algorithm. We use multiple trackers for estimating state variables related to global positioning, camera and the lane, and road arcs respectively. Each tracker samples particles based on the state estimates by its predecessor. Experiments with real road videos demonstrate the effectiveness of the approach and the improvement to global positioning.