TY - GEN
T1 - Support and monitoring trajectory paths for vehicles using mobile devices
AU - Ojeda-Andablo, Jenny A.
AU - Paz-Trejo, Javier
AU - Carvajal-Gámez, Blanca E.
AU - Ferrer-Tenorio, Jorge
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/3/21
Y1 - 2016/3/21
N2 - Currently artificial intelligence combined with computer vision has helped in performing daily activities, the current tendency of semi-autonomous vehicles assisted by artificial intelligence and computer vision has created a new field of image processing research. The image processing to support autonomous route tracking systems they have been tested in controlled media. In this investigation we perform the implementation of a system of guide and trajectory tracking in real scenarios (not controlled media) as road and city streets. Propose a new method for detecting lines as the Hough transform, in a simple and efficient for the detection of lane on a mobile manner. With the above determined whether or not the vehicle conserves his lane by opening angles of triangles detected with the mobile camera, to support and assist the driver. The methodology is tested to determine its performance qualitatively and quantitatively.
AB - Currently artificial intelligence combined with computer vision has helped in performing daily activities, the current tendency of semi-autonomous vehicles assisted by artificial intelligence and computer vision has created a new field of image processing research. The image processing to support autonomous route tracking systems they have been tested in controlled media. In this investigation we perform the implementation of a system of guide and trajectory tracking in real scenarios (not controlled media) as road and city streets. Propose a new method for detecting lines as the Hough transform, in a simple and efficient for the detection of lane on a mobile manner. With the above determined whether or not the vehicle conserves his lane by opening angles of triangles detected with the mobile camera, to support and assist the driver. The methodology is tested to determine its performance qualitatively and quantitatively.
KW - Autonomous vehicles
KW - Hough transform
KW - artificial intelligence
KW - computer vision
KW - tracking lines
UR - http://www.scopus.com/inward/record.url?scp=84966521939&partnerID=8YFLogxK
U2 - 10.1109/CONIELECOMP.2016.7438571
DO - 10.1109/CONIELECOMP.2016.7438571
M3 - Contribución a la conferencia
AN - SCOPUS:84966521939
T3 - 2016 International Conference on Electronics, Communications and Computers, CONIELECOMP 2016
SP - 174
EP - 179
BT - 2016 International Conference on Electronics, Communications and Computers, CONIELECOMP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Electronics, Communications and Computers, CONIELECOMP 2016
Y2 - 24 February 2016 through 26 February 2016
ER -