Title :
Learning Activity-Based Ground Models from a Moving Helicopter Platform
Author :
Lookingbill, Andrew ; Lieb, David ; Stavens, David ; Thrun, Sebastian
Author_Institution :
Stanford AI Lab Stanford University Stanford, CA 94305; apml@stanford.edu
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.
Keywords :
Activity Maps; Computer Vision; Machine Learning; Object Tracking; Particle Filters; Artificial intelligence; Cameras; Helicopters; Histograms; Layout; Mobile robots; Particle filters; Particle tracking; Probability distribution; Roads; Activity Maps; Computer Vision; Machine Learning; Object Tracking; Particle Filters;
Conference_Titel :
Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on
Print_ISBN :
0-7803-8914-X
DOI :
10.1109/ROBOT.2005.1570724
