ICRA 2011 Paper Abstract

Close

Paper TuP204.2

Gleason, Joshua (University of Nevada Reno), Nefian, Ara (NASA Ames Research Center), Bouyssounousse, Xavier (NASA Ames Research Center), Fong, Terrence (NASA Ames Research Center (ARC)), bebis, george (University of Nevada)

Vehicle Detection from Aerial Imagery

Scheduled for presentation during the Regular Sessions "Recognition I" (TuP204), Tuesday, May 10, 2011, 15:40−15:55, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Recognition, Visual Learning, Computer Vision for Robotics and Automation

Abstract

Vehicle detection from aerial images is becoming an increasingly important research topic in surveillance, traffic monitoring and military applications. The system described in this paper focuses on vehicle detection in rural environments and its applications to oil and gas pipeline threat detection. Automatic vehicle detection by unmanned aerial vehicles (UAV) will replace current pipeline patrol services that rely on pilot visual inspection of the pipeline from low altitude high risk flights that are often restricted by weather conditions. Our research compares a set of feature extraction methods applied for this specific task and four classification techniques. The best system achieves an average 85% vehicle detection rate and 1800 false alarms per flight hour over a large variety of areas including vegetation, rural roads and buildings, lakes and rivers collected during several day time illuminations and seasonal changes over one year.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-03-30  00:24:36 PST  Terms of use