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Proceedings Paper

Classification of car in lane using support vector machines
Author(s): Michael Del Rose; David J. Gorsich; Robert E. Karlsen
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Paper Abstract

Support Vector Machines (SVMs) have become popular due to their accuracy in classifying sparse data sets. Their computational time can be virtually independent of the size of the feature vector. SVMs have been shown to out perform other learning machines on many data sets. In this paper, we use SVMs to detect a car in a lane of traffic. Digital pictures of various driving situations are used. The results from the SVM algorithm are compared to results from a standard neural network approach.

Paper Details

Date Published: 13 October 2000
PDF: 7 pages
Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); doi: 10.1117/12.403633
Show Author Affiliations
Michael Del Rose, U.S. Army Tank-Automotive Research, Development and Engineering Ctr. (United States)
David J. Gorsich, U.S. Army Tank-Automotive Research, Development and Engineering Ctr. (United States)
Robert E. Karlsen, U.S. Army Tank-Automotive Research, Development and Engineering Ctr. (United States)


Published in SPIE Proceedings Vol. 4120:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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