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

PABLO NEGRI

IET IMAGE PROCESSING

INST ENGINEERING TECHNOLOGY-IET

Año: 2014 vol. 8 p. 406 - 416

1751-9659

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 0.1 false positives per image for highly occluded images.