Open AccessEstimating the queue length at street intersections by using a movement feature space approach
Author(s) -
Negri Pablo
Publication year - 2014
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0496
Subject(s) - support vector machine , histogram , artificial intelligence , histogram of oriented gradients , feature vector , boosting (machine learning) , queue , pattern recognition (psychology) , computer science , computer vision , feature (linguistics) , false positive paradox , feature extraction , image (mathematics) , linguistics , philosophy , programming language
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.