Classification of Car in Lane Using Support Vector Machines

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 2000
Accession Number
ADA572877

Entities

People

  • David J. Gorsich
  • Michael Del Rose
  • Robert Karlsen

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence Software
  • Cameras
  • Classification
  • Data Science
  • Data Sets
  • Digital Cameras
  • Dimensionality Reduction
  • Engineering
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Simulations
  • Standards
  • Supervised Machine Learning
  • Wavelet Transforms

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML