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

Eric Hsueh Chan Lu, Kuei Hua Chang, Jing Mei Ciou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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%.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 12th Asian Conference, ACIIDS 2020, Proceedings
EditorsNgoc Thanh Nguyen, Bogdan Trawinski, Kietikul Jearanaitanakij, Suphamit Chittayasothorn, Ali Selamat
PublisherSpringer
Pages545-552
Number of pages8
ISBN (Print)9783030419639
DOIs
Publication statusPublished - 2020 Jan 1
Event12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020 - Phuket, Thailand
Duration: 2020 Mar 232020 Mar 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12033 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020
CountryThailand
CityPhuket
Period20-03-2320-03-26

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lu, E. H. C., Chang, K. H., & Ciou, J. M. (2020). Data Pre-processing Based on Convolutional Neural Network for Improving Precision of Indoor Positioning. In N. T. Nguyen, B. Trawinski, K. Jearanaitanakij, S. Chittayasothorn, & A. Selamat (Eds.), Intelligent Information and Database Systems - 12th Asian Conference, ACIIDS 2020, Proceedings (pp. 545-552). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12033 LNAI). Springer. https://doi.org/10.1007/978-3-030-41964-6_47