dc.contributor.advisor | Midtbø, Terje | |
dc.contributor.advisor | Vinje, Jan-Erik | |
dc.contributor.author | Jakobsen, Kristina | |
dc.date.accessioned | 2019-09-11T08:14:59Z | |
dc.date.created | 2018-07-12 | |
dc.date.issued | 2018 | |
dc.identifier | ntnudaim:20165 | |
dc.identifier.uri | http://hdl.handle.net/11250/2614657 | |
dc.description.abstract | Augmented Reality (AR) technologies are becoming more available than ever. What the technologies lack, is a system making it possible for users to share and communicate across all the different AR platforms. This is the motivation behind the AR-Cloud and the open source AR-Cloud community Open AR-Cloud. A research and development program aims to find optimal ways to automatically estimate the geographical position and orientation of AR-devices GeoPose using object detection on a phone and a continuously updated model of the world. There is a rising interest in building small and efficient Neural Networks for mobile deployment. This master thesis has investigated real-time object detection on an Android device by looking into how a combination of a dataset and an object detecting method will solve the task of identifying stationary objects found in an everyday scene as a step towards automatic GeoPose estimation. The chosen method achieves an acceptable inference speed of five frames per second on a Samsung s9 and a mAP of 33,46%. | en |
dc.language | eng | |
dc.publisher | NTNU | |
dc.subject | Ingeniørvitenskap og IKT, Geomatikk | en |
dc.title | Real-time object detection on Android devices - A step towards automatic GeoPose estimation on mobile AR platforms | en |
dc.type | Master thesis | en |
dc.source.pagenumber | 95 | |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap,Institutt for bygg- og miljøteknikk | nb_NO |
dc.date.embargoenddate | 10000-01-01 | |