Open AccessMonovision‐based vehicle detection, distance and relative speed measurement in urban traffic
Author(s) -
Arenado Manuel Ibarra,
Oria Juan Maria Pérez,
TorreFerrero Carlos,
Rentería Luciano Alonso
Publication year - 2014
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2013.0098
Subject(s) - thresholding , computer vision , robustness (evolution) , artificial intelligence , histogram , computer science , shadow (psychology) , radar , intelligent transportation system , vehicle tracking system , segmentation , engineering , image (mathematics) , transport engineering , psychology , telecommunications , biochemistry , chemistry , psychotherapist , gene
This study presents a monovision‐based system for on‐road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an intelligent transportation system. The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR‐based technologies. The algorithm is divided in three major stages. For vehicle detection, the authors use a combination of two features: the shadow underneath the vehicle and horizontal edges. They propose a new method for shadow thresholding based on the grey‐scale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardised in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.