Title :
A practical system for road marking detection and recognition
Author :
Tao Wu ; Ranganathan, A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Abstract :
We present a system for detecting and recognizing road markings from video input obtained from an in-car camera. Our system learns feature-based templates of road markings from training data and matches these templates to detected features in the test images during runtime. We use MSER features and perform the template matching in an efficient manner so that our system can detect multiple road markings in a single image. Our system also scales well with the number of categories of road markings to be detected. For evaluating our system, we present an extensive dataset (available from www.ananth.in/RoadMarkingDetection.html) of road markings with ground truth labels, which we hope will be useful as a benchmark dataset for future researchers in this area.
Keywords :
automobiles; cameras; feature extraction; image matching; learning (artificial intelligence); object detection; object recognition; traffic engineering computing; video signal processing; MSER features; feature detection; feature-based template learning; ground truth labels; in-car camera; practical system; road marking detection; road marking recognition; template matching; test images; training data; video input; Cameras; Feature extraction; Image edge detection; Roads; Shape; Vectors; Vehicles;
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2012 IEEE
Conference_Location :
Alcala de Henares
Print_ISBN :
978-1-4673-2119-8
DOI :
10.1109/IVS.2012.6232144
