Vehicle Detection from Aerial Imagery

摘要：

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.

作者: Joshua Gleason Ara V. Nefian Xavier Bouyssounousse Terry Fong George Bebis

作者单位: University of Nevada Reno Carnegie Mellon University and NASA Ames Research Center NASA Ames Research Center

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 2065-2070

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）