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

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dc.contributor.authorBerggren, Andreas
dc.contributor.authorBjörklund, Eric
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillämpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T12:53:08Z
dc.date.available2019-07-03T12:53:08Z
dc.date.issued2012
dc.description.abstractThis 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.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/160327
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2012:28
dc.setspec.uppsokTechnology
dc.subjectSignalbehandling
dc.subjectHållbar utveckling
dc.subjectProduktion
dc.subjectSignal Processing
dc.subjectSustainable Development
dc.subjectProduction
dc.titleActive Vision System with Human Detection - Using RGB-D images and machine learning algorithms
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeApplied physics (MPAPP), MSc
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