Estimating the queue length at street intersections by using a movement feature space approach

• Autores: Negri, Pablo Augusto
• Año de publicación: 2014
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: Vehicle Detection; Movement Feature Space; Histogram of Oriented Level Lines
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

This study aims to estimate the traffic load at street intersections obtaining the circulating vehicle number through image processing and pattern recognition. The algorithm detects moving objects in a street view by using level lines and generates a new feature space called movement feature space (MFS). The MFS generates primitives as segments and corners to match vehicle model generating hypotheses. The MFS is also grouped in a histogram configuration called histograms of oriented level lines (HO2 L). This work uses HO2 L features to validate vehicle hypotheses comparing the performance of different classifiers: linear support vector machine (SVM), non-linear SVM, neural networks and boosting. On average, successful detection rate is of 86% with 10-1 false positives per image for highly occluded images.
Fil: Negri, Pablo Augusto. Universidad Argentina de la Empresa. Facultad de Ingeniería y Ciencias Exactas. Instituto de Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina