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Title Detection, Recognition, and Georeferencing of Traffic Signs in Street-Level Imagery
Author Henrik, Møller Rasmussen
Johan, Musaeus Bruun
Supervisor Aanæs, Henrik (Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2010
Abstract For road equipment maintenance, an automated indexing system is desirable. As a pilot project for such a system, opportunities for detecting, recognizing and georeferencing traffic signs in GPS-annotated street-level imagery are investigated. Five important and frequent sign categories are targeted and a colorand shape-based approach for detecting these is implemented. For recognizing traffic signs, i.e. identifying pictograms, experiments with a multilayer perceptron neural network on normalized images are performed. Finally an intuitive approach for georeferencing signs using spherical geometry and geographical data without distance measurements is developed. The detection works very well on warning, yield, and prohibition signs, and decently on mandatory and information signs, although the latter group has a higher rate of false positives. The recognition experiments show that classifying pictograms using multilayer perceptrons seems possible. Since the amount of data was limited, experiments with additional data are required in order to provide a more robust evaluation. The intuitive approach for georeferencing shows great results despite factors such as car roll and pitch, lens distortion, and object elevation being ignored. Based on the investigations and experimental results, a fully automated sign indexing system denitely seems achievable.
Imprint Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark
Series IMM-M.Sc.-2010-84
Original PDF ep10_84.pdf (95.12 MB)
Admin Creation date: 2010-10-11    Update date: 2010-10-11    Source: dtu    ID: 267731    Original MXD