Advertisement

Data Pre-processing Based on Convolutional Neural Network for Improving Precision of Indoor Positioning

  • Eric Hsueh-Chan LuEmail author
  • Kuei-Hua Chang
  • Jing-Mei Ciou
Conference paper
  • 314 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

In the past, indoor positioning technology was mainly based on pedestrian dead reckoning and wireless signal positioning methods, but it was easy to cause some problems such as error accumulation and signal interference. Positioning accuracy still needs to be improved. With the development of neural networks in recent years, many researchers have successfully applied the neural network to the indoor positioning problem based on the Convolutional Neural Network (CNN). This technique mainly determines the position of the image by matching the image features. CNN faces the same challenges as other supervised learning. If the “clean” data cannot be collected, the trained model will not achieve good positioning accuracy. For CNN used for indoor positioning, if someone passes through in the training data, causing the person to appear in different positions of the images, the model may think that the images are the same location. To solve this problem, we propose a data pre-processing method to improve the accuracy of indoor positioning based on CNN. In this method, the moving objects recognized in training and testing data are modified in different ways. We perform data pre-processing method based on Mask R-CNN and YOLO, and then integrate the pre-processing method to PoseNet the famous CNN indoor positioning architecture. Through real experimental analysis, removing moving objects can effectively improve indoor positioning accuracy about 46%.

Keywords

Convolutional Neural Network Indoor positioning Object detection Data pre-processing 

Notes

Acknowledgment

This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 108-2621-M-006-008 -.

References

  1. 1.
    Ciou, J.-M., Lu, E.H.-C.: Indoor positioning using convolution neural network to regress camera pose, ISPRS Geospatial Week, 1289–1294 (2019) Google Scholar
  2. 2.
    Grossmann, U., Gansemer, S., Suttorp, O.: RSSI-based WLAN indoor positioning used within a digital museum guide. Int. J. Comput. 7(2), 66–72 (2014)Google Scholar
  3. 3.
    Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: IEEE International Conference on Computer Vision, pp. 2938–2946 (2015)Google Scholar
  4. 4.
    Lan, K.-C., Shih, W.-Y.: An indoor locationtracking system for smart parking. Int. J. Parallel Emergent Distrib. Syst. 29(3), 215–238 (2014)CrossRefGoogle Scholar
  5. 5.
    Lu, E.H.-C., Chen, H.-S., Tseng, V.S.: An efficient framework for multirequest route planning in urban environments. IEEE Trans. Intell. Transp. Syst. 18(4), 869–879 (2017)CrossRefGoogle Scholar
  6. 6.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  7. 7.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 91–99 (2015)Google Scholar
  8. 8.
    Subhan, F., Hasbullah, H., Rozyyev, A., Bakhsh, S.T.: Indoor positioning in bluetooth networks using fingerprinting and lateration approach. In: IEEE International Conference on Information Science and Applications, pp. 1–9 (2011)Google Scholar
  9. 9.
    Szegedy, C., et al.: Going deeper with convolutions. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  10. 10.
    Tseng, V.S., Lu, E.H.-C., Huang, C.-H.: Mining temporal mobile sequential patterns in location-based service environments. In: IEEE International Conference on Parallel and Distributed Systems, pp. 1–8 (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of GeomaticsNational Cheng Kung UniversityTainan CityTaiwan (R.O.C.)

Personalised recommendations