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A Lightweight Indoor Location Method Based on Image Fingerprint

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12239))

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Abstract

Image-based localization is featured with low deployment cost and universal accessibility with smartphones, thus attracts great attentions from both academy and industry. The fingerprints from the image data, which mostly consists of the image features, are promising for indoor localization. However, constructing such fingerprint database is usually time-consuming and labor-intensive. Moreover the high computational and storage cost render this localization scheme highly relies on the cloud-based solution. Meanwhile, this solution suffers from the transmission delay and query contentions in the cloud. To this end, we propose a smartphone-based indoor localization method with enhanced image fingerprint extraction and edge computing paradigm. Basically, we extract image feature vectors with MobileNet and offload part of the database to the smartphone. Then, we query image through the second-level retrieval and verification of the smartphone orientation information to finds out six most similar images. After this, we obtain the most similar image by carrying out query expansion. In the phase of collecting image database, video and samrtphone sensors data are collaboratively used to build image database. We have implement the prototype system on the Android platform, and conduct real experiments to verify the feasibility of the positioning method. Experiment results show that our method has good accuracy (90% location error is within 1.5 m) and high real-time performance (average location delay is about 1.11 s) .

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Authors and Affiliations

  1. Collage of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Ran Gao & Yanchao Zhao

Authors
  1. Ran Gao
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  2. Yanchao Zhao
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Corresponding author

Correspondence to Yanchao Zhao .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Gao, R., Zhao, Y. (2020). A Lightweight Indoor Location Method Based on Image Fingerprint. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_67

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  • DOI: https://doi.org/10.1007/978-3-030-57884-8_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57883-1

  • Online ISBN: 978-3-030-57884-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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