Details
| Original language | English |
|---|---|
| Title of host publication | Information Science and Applications 2017 - ICISA 2017 |
| Editors | Kuinam Kim, Nikolai Joukov |
| Publisher | Springer Verlag |
| Pages | 76-83 |
| Number of pages | 8 |
| ISBN (print) | 9789811041532 |
| Publication status | Published - 18 Mar 2017 |
| Event | 8th International Conference on Information Science and Applications, ICISA 2017 - Macau, China Duration: 20 Mar 2017 → 23 Mar 2017 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 424 |
| ISSN (Print) | 1876-1100 |
| ISSN (electronic) | 1876-1119 |
Abstract
Determination of the absolute geographical position has become every day routine, using the Global Positioning System (GPS), despite the prior existence of maps. However no equally universal solution has been developed for determining one’s location inside a building, which is an equally relevant problem statement, for which GPS cannot be used. Existing solutions usually involve additional infrastructure on the end of the location provider, such as beacon installations or particular configurations of wireless access points. These solutions are generally facilitated by additional native mobile applications on the client device, which connect to this infrastructure. We are aware of such solutions, but believe these to be lacking in simplicity. Our approach for indoor positioning alleviates the necessity for additional hardware by the provider, and software installation by the user. We propose to determine the user’s position inside a building using only a photo of the corridor visible to the user, uploading it to a local positioning server, accessible using a browser, which performs a classification of the photo based on a Neural Network approach. Our results prove the feasibility of our approach. One floor of the university’s building with partially very similar corridors has been learned by a deep convolutional neural network. A person lost in the building simply accesses the positioning server’s website and uploads a photo of his current line of sight. The server responds by generating and displaying a map of the building with the user’s current position and current direction.
Keywords
- Indoor positioning, Machine-learning, Neural network, Scene analysis
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
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Information Science and Applications 2017 - ICISA 2017. ed. / Kuinam Kim; Nikolai Joukov. Springer Verlag, 2017. p. 76-83 (Lecture Notes in Electrical Engineering; Vol. 424).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Indoor positioning solely based on user’s sight
AU - Becker, Matthias
N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2017. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/3/18
Y1 - 2017/3/18
N2 - Determination of the absolute geographical position has become every day routine, using the Global Positioning System (GPS), despite the prior existence of maps. However no equally universal solution has been developed for determining one’s location inside a building, which is an equally relevant problem statement, for which GPS cannot be used. Existing solutions usually involve additional infrastructure on the end of the location provider, such as beacon installations or particular configurations of wireless access points. These solutions are generally facilitated by additional native mobile applications on the client device, which connect to this infrastructure. We are aware of such solutions, but believe these to be lacking in simplicity. Our approach for indoor positioning alleviates the necessity for additional hardware by the provider, and software installation by the user. We propose to determine the user’s position inside a building using only a photo of the corridor visible to the user, uploading it to a local positioning server, accessible using a browser, which performs a classification of the photo based on a Neural Network approach. Our results prove the feasibility of our approach. One floor of the university’s building with partially very similar corridors has been learned by a deep convolutional neural network. A person lost in the building simply accesses the positioning server’s website and uploads a photo of his current line of sight. The server responds by generating and displaying a map of the building with the user’s current position and current direction.
AB - Determination of the absolute geographical position has become every day routine, using the Global Positioning System (GPS), despite the prior existence of maps. However no equally universal solution has been developed for determining one’s location inside a building, which is an equally relevant problem statement, for which GPS cannot be used. Existing solutions usually involve additional infrastructure on the end of the location provider, such as beacon installations or particular configurations of wireless access points. These solutions are generally facilitated by additional native mobile applications on the client device, which connect to this infrastructure. We are aware of such solutions, but believe these to be lacking in simplicity. Our approach for indoor positioning alleviates the necessity for additional hardware by the provider, and software installation by the user. We propose to determine the user’s position inside a building using only a photo of the corridor visible to the user, uploading it to a local positioning server, accessible using a browser, which performs a classification of the photo based on a Neural Network approach. Our results prove the feasibility of our approach. One floor of the university’s building with partially very similar corridors has been learned by a deep convolutional neural network. A person lost in the building simply accesses the positioning server’s website and uploads a photo of his current line of sight. The server responds by generating and displaying a map of the building with the user’s current position and current direction.
KW - Indoor positioning
KW - Machine-learning
KW - Neural network
KW - Scene analysis
UR - http://www.scopus.com/inward/record.url?scp=85016135517&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-4154-9_10
DO - 10.1007/978-981-10-4154-9_10
M3 - Conference contribution
AN - SCOPUS:85016135517
SN - 9789811041532
T3 - Lecture Notes in Electrical Engineering
SP - 76
EP - 83
BT - Information Science and Applications 2017 - ICISA 2017
A2 - Kim, Kuinam
A2 - Joukov, Nikolai
PB - Springer Verlag
T2 - 8th International Conference on Information Science and Applications, ICISA 2017
Y2 - 20 March 2017 through 23 March 2017
ER -