Road tracking in aerial images based on human–computer interaction and Bayesian filtering

A typical way to update map road layers is to compare recent aerial images with existing map data, detect new roads and add them as cartographic entities to the road layer. This method cannot be fully automated because computer vision algorithms are still not sufficiently robust and reliable. More importantly, maps require final checking by a human due to the legal implications of errors. In this paper we introduce a road tracking system based on human–computer interactions (HCI) and Bayesian filtering. Bayesian filters, specifically, extended Kalman filters and particle filters, are used in conjunction with human inputs to estimate road axis points and update the tracking algorithms. Experimental results show that this approach is efficient and reliable and that it produces substantial savings over the traditional manual map revision approach. The main contribution of the paper is to propose a general and practical system that optimizes the performance of road tracking when both human and computer resources are involved.