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Indoor positioning: “an image-based crowdsource machine learning approach”

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Abstract

Various technologies have been utilized today for recognizing client or user in the indoor areas. These technologies incorporate RSSI, Bluetooth Low Energy Beacons, Ultrasound waves, Vision-based advances, for example, fixed camera recordings QR codes, remote gadgets, etc. RSSI fingerprinting technique requires more effort and it is also expensive to be used for indoor localization frameworks working in real-time. In this research, indoor localization based on images is investigated as an option in contrast to other indoor positioning techniques using these days. Image-based indoor positioning is more affordable than RSSI based technologies being utilized. A mobile phone camera is utilized to take the pictures of area inside the building to find the user inside the building. Sensor data from various sensors isn’t required or no extra framework is required to find the client in the building utilizing indoor positioning based on an image. Microsoft Azure Custom Vision Services are utilized to locate the client; MS Azure classifies the pictures in one of the labels made. Strategy’s attainability is demonstrated by various investigations and accomplished accuracy and review is recorded above 90%. The average precision of the trained model is recorded above 95%.

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References

  1. Bano G et al (2019) Comparative analysis of Mobile application testing and crowd source software testing, 2019 8th international conference on information and communication technologies (ICICT). Karachi, Pakistan, pp 129–134. https://doi.org/10.1109/ICICT47744.2019.9001991

    Book  Google Scholar 

  2. Bay H, Ess A, Tuytelaars T, & Gool LV (2008) Speeded-up robust features (SURF), Computer Vision and Image Understanding.110:346–359.

  3. Caso G, Nardis LD, Benedetto MD (2019) Low-complexity offline and online strategies for Wi-fi fingerprinting indoor positioning systems

    Book  Google Scholar 

  4. Deak G, Curran K, Condell J (2012) A survey of active and passive indoor localisation systems. Comput Commun 35:1939–1954

    Article  Google Scholar 

  5. Goldoni E, Savioli A, Risi M, Gamba P (2010) Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4. 2010 European Wireless Conference (EW), pp. 71-77.

  6. Huang Y, Wang H, Zhan K, Zhao J, Gui P, Feng T (2015) Image-based localization for indoor environment using mobile phone,

  7. Image matching and image searching using feature extraction through OpenCV to construct indoor maps as described by Huang et al. (2015). RSSI to construct radio map and user localization as mentioned by Jung, Lee, and Han (2018) and Wu, Yang and Liu (2015). Image matching and map based on feature extraction as described by Werner, Kessel and Marouane (2011).

  8. Insoft (2019) , Indoor positioning system , https://www ,infsoft ,com/solutions/basics/quick-start-indoor-positioning

  9. Jung S, Lee G, Han D (2018) Methods and tools to construct a global indoor positioning system. IEEE Transactions on Systems Man and Cybernetics: Systems 48:906–919

    Article  Google Scholar 

  10. Kamangar, Zainab & Kamangar, Umair & Ali, Qasim & Siddiqui, Isma & Nizamani, Shahzad & Ali, Tauha. (2019). To enhance effectiveness of crowdsource software testing by applying personality types. ICSIE '19: Proceedings of the 2019 8th International Conference on Software and Information Engineering. 15–19. https://doi.org/10.1145/3328833.3328838.

  11. Kawaji H, Hatada K, Yamasaki T, Aizawa K (2010) Image-based indoor positioning system: fast image matching using omnidirectional panoramic images

  12. Khowaja SS et al (2020) Crowdsourced machine learning based recommender for software design patterns. International Journal of Computer (IJC) 36(1):34–52

    Google Scholar 

  13. Koyuncu H, Yang S (2010) A survey of indoor positioning and object locating systems. IJCSNS Int, J, Computer Science Network Security 10:121–128

    Google Scholar 

  14. Lategahn H, Stiller C (2012) City GPS using stereo vision, 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012). 1-6

  15. Lategahn H, Stiller C (2014) Vision-only localization. IEEE Trans Intell Transp Syst 15:1246–1257

    Article  Google Scholar 

  16. Li N, Becerik-Gerber B (2011) Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Adv Eng Inform 25:535–546

    Article  Google Scholar 

  17. Li Y, Snavely N, & Huttenlocher DP (2010) Location recognition using prioritized feature matching, Computer Vision–ECCV. 791–804

  18. Malavalli R, (2017), Indoor localization through machine learning on WiFi fingerprints

    Google Scholar 

  19. Mascharka D, & Manley ED (2015) Machine learning for indoor localization using mobile phone-based sensors

  20. Nuaimi KA, Kamel H (2011) A survey of indoor positioning systems and algorithms. International Conference on Innovations in Information Technology 2011:185–190

  21. Peng, Y., Niu, X., Tang, J., Mao, D., Qian, C. (2018). Fast signals of opportunity fingerprint database maintenance with autonomous unmanned ground vehicle for indoor positioning. Sensors.

  22. Sana (2013) A survey of indoor localization techniques. IOSR J, Electrical Electron Eng 6:69–76

    Article  Google Scholar 

  23. Sattler T, Leibe B, Kobbelt L (2011) Fast image-based localization using direct 2D-to-3D matching. International Conference on Computer Vision 2011:667–674

  24. Shahid E, Arain QA, Kumari S , & Farah I (2019) Images based indoor positioning using AI and crowdsourcing. ICEIT 2019

  25. Sinha D, Ahmed MT, Greenspan M (2014) Image retrieval using landmark indexing for indoor navigation. Canadian Conference on Computer and Robot Vision 2014:63–70

  26. Taj, Soonh & Ali, Qasim & Memon, Imran & Zubedi, Asma. (2019). To apply data mining for classification of crowd sourced software requirements. 42–46. https://doi.org/10.1145/3328833.3328837.

  27. Werner M, Kessel M, Marouane C (2011) Indoor positioning using smartphone camera. International Conference on Indoor Positioning and Indoor Navigation 2011:1–6

  28. Wu C, Yang Z, Liu Y (2015) Smartphones based crowdsourcing for indoor localization. IEEE Trans Mob Comput 14:444–457

    Article  Google Scholar 

  29. Zheng X, Bao G, Fu R, Pahlavan K (2012) The performance of simulated annealing algorithms for Wi-fi localization using Google indoor map. IEEE Vehicular Technology Conference (VTC Fall) 2012:1–5

  30. Zhou B,Li Q, Mao Q, & Tu W (2017) A robust crowdsourcing-based indoor localization system , sensors.

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Authors and Affiliations

  1. Department of Software Engineering, Mehran University of Engineering & Technology, Jamshoro, Sindh, Pakistan

    Eman Shahid & Qasim Ali Arain

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  1. Eman Shahid
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  2. Qasim Ali Arain
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Correspondence to Qasim Ali Arain.

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Shahid, E., Arain, Q.A. Indoor positioning: “an image-based crowdsource machine learning approach”. Multimed Tools Appl 80, 26213–26235 (2021). https://doi.org/10.1007/s11042-021-10906-z

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  • DOI: https://doi.org/10.1007/s11042-021-10906-z

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