Fast shadow detection for urban autonomous driving applications
Name
Park-2009-Fast shadow detection for urban autonomous driving applications.pdf
Size
377.76 KB
Format
Adobe PDF
Checksum (MD5)
ff8a976012a79bcd9f47b74181f047ab
Author(s)
Park, Sooho
Lim, Sejoon
Date Issued
December 2009
Journal
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Park, Sooho, and Sejoon Lim. “Fast shadow detection for urban autonomous driving applications.” IEEE, 2009. 1717-1722. Web. 21 Dec. 2011. © 2009 Institute of Electrical and Electronics Engineers
Version
Final published version
Abstract
This paper presents shadow detection methods for vision-based autonomous driving in an urban environment. Shadows misclassified as objects create problems in autonomous driving applications. Real-time efficient algorithms in dynamic background settings are proposed. Without the static background assumption, which was often used in previous work to develop fast algorithms, our scheme estimates the varying background efficiently. A combination of various features classifies each pixel into one of the following categories: road, shadow, dark object, or other objects. In addition to pixel level classification, spatial context is also used to identify the shadows. Our results show that our methods perform well for autonomous driving applications and are fast enough to work in real time.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2009.5354613
