DeepMoVIPS: Visual indoor positioning using transfer learning

Finding the location of a mobile user is a classical and important problem in pervasive computing, because location provides a lot of information about the situation of a person from which adaptive computer systems can be created. While the inference of location outside buildings is possible with GPS or similar satellite systems, these are unavailable inside buildings. A large number of methods has been proposed to overcome this limitation and provide indoor location to mobile devices such as smartphones. With this paper, we propose a novel visual indoor positioning system DeepMoVIPS, which exploits the image classification power of deep convolutional neural networks for symbolic indoor geolocation. We further show, how to transfer visual features from deep learned networks to the application domain and give encouraging results of more than 95% classification accuracy for datasets modelling work environments using 16 rooms and evaluation over a time frame of four weeks.

[1]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Samer S. Saab,et al.  A Standalone RFID Indoor Positioning System Using Passive Tags , 2011, IEEE Transactions on Industrial Electronics.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  Kai Wang,et al.  An accurate indoor localization approach using cellphone camera , 2015, 2015 11th International Conference on Natural Computation (ICNC).

[7]  Chong-Wah Ngo,et al.  Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.

[8]  Martin Werner,et al.  SMARTPOS: Accurate and Precise Indoor Positioning on Mobile Phones , 2011 .

[9]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[10]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[11]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[12]  M. Matsumoto,et al.  RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering , 2007 .

[13]  Kiyoharu Aizawa,et al.  Image-based indoor positioning system: fast image matching using omnidirectional panoramic images , 2010, MPVA '10.

[14]  Chadly Marouane,et al.  Indoor positioning using smartphone camera , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[15]  Gaetano Borriello,et al.  Information Overlay for Camera Phones in Indoor Environments , 2007, LoCA.

[16]  Gérard Lachapelle,et al.  GNSS Indoor Location Technologies , 2004 .