IEICE TRANSACTIONS on Fundamentals
A Robust Indoor/Outdoor Detection Method Based on Spatial and Temporal Features of Sparse GPS Measured Positions
Summary :
Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.6 pp.860-865
- Publication Date
- 2019/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.860
- Type of Manuscript
- LETTER
- Category
- Intelligent Transport System
Authors
Sae IWATA
Waseda University
Kazuaki ISHIKAWA
Zenrin DataCom Co., LTD.
Toshinori TAKAYAMA
Zenrin DataCom Co., LTD.
Masao YANAGISAWA
Waseda University
Nozomu TOGAWA
Waseda University
Keyword
Latest Issue
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Sae IWATA, Kazuaki ISHIKAWA, Toshinori TAKAYAMA, Masao YANAGISAWA, Nozomu TOGAWA, "A Robust Indoor/Outdoor Detection Method Based on Spatial and Temporal Features of Sparse GPS Measured Positions" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 6, pp. 860-865, June 2019, doi: 10.1587/transfun.E102.A.860.
Abstract: Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.860/_p
Copy
@ARTICLE{e102-a_6_860,
author={Sae IWATA, Kazuaki ISHIKAWA, Toshinori TAKAYAMA, Masao YANAGISAWA, Nozomu TOGAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Robust Indoor/Outdoor Detection Method Based on Spatial and Temporal Features of Sparse GPS Measured Positions},
year={2019},
volume={E102-A},
number={6},
pages={860-865},
abstract={Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.},
keywords={},
doi={10.1587/transfun.E102.A.860},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - A Robust Indoor/Outdoor Detection Method Based on Spatial and Temporal Features of Sparse GPS Measured Positions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 860
EP - 865
AU - Sae IWATA
AU - Kazuaki ISHIKAWA
AU - Toshinori TAKAYAMA
AU - Masao YANAGISAWA
AU - Nozomu TOGAWA
PY - 2019
DO - 10.1587/transfun.E102.A.860
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2019
AB - Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.
ER -
English
