Applied Informatics Group

Innenraumklassifikation mit Hilfe von 3D Daten

TitleInnenraumklassifikation mit Hilfe von 3D Daten
Publication TypeDiploma Theses
AuthorsNitz, P.

When a person enters a room, he mostly has an immediate notion what kind of room he is in. He knows immediately whether he is in a living room, office, bedroom, hall or kitchen. It would be nice if robots could also orient themself faster in human surroundings. The aim of my work is that a plausible estimation can be made to identify the type of room. This should be achieved with the help of a single frame of a room by a 3D Time-of-Flight (Tof) camera. The geometrical data of the camera is preprocessed and the points of a room which lie on a planar structure are combined. On these planes different feature vectors are calculated and with their help rooms can be classified. Until now the system worked with 4 different feature vectors (number of points per patch, angle between patches, angle between neighbouring patches, ratio between sizes of patches). My job will be to define new feature vectors based on the planes and to evaluate them with regard to their performance in classification and recognition of the different room types. Ada-Boost could be used here as a possible technique. For classification a Support Vector Machine (SVM) or the k-Next-Neighbour-function (kNN) can be used. The existing data base (two flats with 5 different classes) for training and testing is expandable and should be extended in a suitable manner.

Supervised BySwadzba, Agnes, and Wachsmuth, Sven
Academic DepartmentFaculty of Technology
UniversityBielefeld University
DA Innenklassifikation mit 3D Daten.pdf37.22 MB

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