Learning Activity-Based Ground Models from a Moving Helicopter Platform

Andrew Lookingbill, David Lieb, David Stavens, Sebastian Thrun

Abstract: We present a method for learning activity-based ground models based on a multiple particle filter approach to motion tracking in video acquired from a moving aerial platform. Such models offer a number of potential benefits. In this paper we demonstrate the ability of activity-based models to improve the performance of an object motion tracker as well as their applicability to global registration of video sequences.