Volume IV-4/W3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W3, 13-19, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W3-13-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W3, 13-19, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W3-13-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 Sep 2017

25 Sep 2017

EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

F. Arce1, E. Zamora2, G. Hernández1, and H. Sossa1 F. Arce et al.
  • 1Instituto Politécnico Nacional, CIC, Av. Juan de Dios Bátiz S/N, Col. Nueva Industrial Vallejo, Gustavo A. Madero, 07738, Ciudad de México, México
  • 2Instituto Politécnico Nacional, UPIITA, Av. Instituto Politécnico Nacional 2580, Col. Barrio la Laguna Ticomán, 07340, Ciudad de México, México

Keywords: Artificial Neural Networks, Ellipsoidal Neuron, Dendritic Processing, Lane Detection

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.