DocumentCode
2943332
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
fYear
2005
fDate
18-22 April 2005
Firstpage
3948
Lastpage
3953
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on
Print_ISBN
0-7803-8914-X
Type
conf
DOI
10.1109/ROBOT.2005.1570724
Filename
1570724
Link To Document