Fast shadow detection for urban autonomous driving applications
Author(s)Park, Sooho; Lim, Sejoon
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009.
Institute of Electrical and Electronics Engineers
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
Final published version