EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

F. Arce, E. Zamora, G. Hernández, H. Sossa

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

Original languageEnglish
Pages (from-to)13-19
Number of pages7
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number4W3
DOIs
StatePublished - 25 Sep 2017
Event2nd International Conference on Smart Data and Smart Cities, UDMS 2017 - Puebla, Mexico
Duration: 4 Oct 20176 Oct 2017

Keywords

  • Artificial Neural Networks
  • Dendritic Processing
  • Ellipsoidal Neuron
  • Lane Detection.

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