Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Moving vehicle classification
Download
index.pdf
Date
2013
Author
Duman, Demet
Metadata
Show full item record
Item Usage Stats
174
views
333
downloads
Cite This
In recent years intelligent transportation systems have been an active research area in computer vision. The aim of this study is to classify moving vehicles from highway videos taken by stationary uncalibrated cameras. For this study, three types of vehicle classes with different scales are chosen to classify: car, van and truck. The proposed algorithm is composed of foreground/background segmentation, feature extraction and classification steps. In order to classify each vehicle, histogram of oriented gradients (HOG) features which are shape-based descriptors and blob features which are dimension-based descriptors are used in the algorithm. The effects of these features on the classification performance are also evaluated and simulation results are given on different highway videos.
Subject Keywords
Image processing
,
Computer vision.
,
Computer algorithms.
URI
http://etd.lib.metu.edu.tr/upload/12616315/index.pdf
https://hdl.handle.net/11511/23037
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Saliency-based visual tracking using correlation filters for surveillance applications /
Tunalı, Emre; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2014)
In recent years intelligent transportation systems (ITS) have been an active research area in computer vision. One of the main goals of ITS is producing systems to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance. Thus, this thesis is devoted to realization of low level tasks, target detection and tracking, for an autonomous video surveillance system. The initial step of the proposed system is moving object detection which is utilized ba...
A 3D topological tracking system for augmented reality
Ercan, Münir; Can, Tolga; Department of Computer Engineering (2010)
Augmented Reality (AR) has become a popular area in computer Science where research studies and technological innovations are extensive. Research in AR first began in the early 1990s and thenceforth, a number of di erent tracking algorithms and methods have been developed. Tracking systems have a critical importance for AR and marker based vision tracking systems became the mostly used tracking systems due to their low cost and ease of use. Basically, marker systems consist of special patterns that are plac...
Automatic vehicle detection and occlusion handling at road intersections
Ülker, Berk; Akar, Gözde; Department of Electrical and Electronics Engineering (2015)
Vision based intelligent transport system applications are extensively utilized and researched in recent years. Several applications with tracking, classification and counting functionalities are used for automatization of traffic management. Work in this thesis aims to provide an accurate vehicle detection method for improving performance of these tasks. Vehicle detection starts with detection of moving objects, using a background subtraction algorithm. Then, accuracy of the foreground mask is improvedusinga...
A control system using behaviour hierarchies and neuro-fuzzy approach
Arslan, Dilek; Alpaslan, Ferda Nur; Department of Computer Engineering (2005)
In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task usin...
Visual object detection and tracking using local convolutional context features and recurrent neural networks
Kaya, Emre Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018)
Visual object detection and tracking are two major problems in computer vision which have important real-life application areas. During the last decade, Convolutional Neural Networks (CNNs) have received significant attention and outperformed methods that rely on handcrafted representations in both detection and tracking. On the other hand, Recurrent Neural Networks (RNNs) are commonly preferred for modeling sequential data such as video sequences. A novel convolutional context feature extension is introduc...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
D. Duman, “Moving vehicle classification,” M.S. - Master of Science, Middle East Technical University, 2013.