In English

Active Vision System with Human Detection - Using RGB-D images and machine learning algorithms

Andreas Berggren ; Eric Björklund
Göteborg : Chalmers tekniska högskola, 2012. Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, ISSN 1652-8557; 2012:28, 2012.
[Examensarbete på avancerad nivå]

This master's thesis will focus on an active safety system for the protection of humans close to commercial construction equipments. The purpose is therefore to propose sensors and algorithms suitable for human detection and furthermore to demonstrate a proof of concept. Early on in the project it was decided to use RGB-D images, which is a conventional color image together with a depth map. This report analyzes both a Kinect sensor and a stereo vision system in order to generate a depth map. Machine learning algorithms were used to classify humans where an artificial neural network was found to be the best performing classifier. Finding informative features is important to facilitate classification. Several imaging features were tested and the six most interesting are presented in this report. The feature called fourier descriptor showed the best performance.

Nyckelord: Human detection, object recognition, computer vision, RGB-D, depth map, Kinect, stereo vision, feature extraction, fourier descriptors, Haar-like features, image moments, machine learning, k-nearest neighbors, support vector machines, decision tree, artificial neural network



Publikationen registrerades 2012-07-10. Den ändrades senast 2013-04-04

CPL ID: 160327

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