@inbook{54ce0c46878e4dd3992506283df5a1dd,
title = "Determination of Congestion Levels Using Texture Analysis of Road Traffic Images",
abstract = "The paper discusses the application of texture analysis of road traffic images for determination of congestion levels. The capability of mapping congestion is investigated using such texture features as: energy, entropy, contrast, homogeneity, dissimilarity, correlation, captured by co-occurrence matrices. No single feature distinctly represents congestion. An optimal combination of features is chosen for classification of congestion levels. Three levels of congestion are correctly differentiated using the proposed texture model of road traffic images. The model is validated using images registered by UAV (Unmanned Aerial Vehicle) flying over a traffic junction.",
keywords = "Congestion, Texture features, Traffic density classification",
author = "Teresa Pamu{\l}a",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2017.",
year = "2017",
doi = "10.1007/978-3-319-43985-3\_5",
language = "English",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "53--61",
booktitle = "Lecture Notes in Networks and Systems",
address = "Germany",
}