Abstract
In this research study, we investigate feasibility of a smart phone based positioning system. The system allows users to get their current location information via their smartphones. The client software required for user smartphones can be distributed and installed easily through standard mobile Apps. Once the mobile App is ready in user smartphones, they just need to take one or two pictures at target objects around, and upload the pictures to the positioning system through Internet connection. The positioning system will identify the location, based on the upload pictures and other related information. We conducted a field test to identify locations among a building complex in a campus. We collected thousands of images taken from outward appearance of several buildings in a campus at different days. The images were classified into 17 classes, based on the location the picture images were taken. From the experiment results, we found that under well control of image quality for both training and testing images the correct classification rate can be as high as 98.3 %. Even under the cases of large scope-of-view mismatching between the raining images and the tested images, the proposed scheme can still generate good correct classification rate (86.7 % and 77.3 % for both covering and covered cases respectively) compared with random guest (1/17 \(=\) 5.88 %).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Leu, J.-S., Tzeng, H.-J.: Received signal strength fingerprint and footprint assisted indoor positioning based on ambient Wi-Fi signals. In: 75th IEEE Vehicular Technology Conference (VTC Spring), pp. 1–5. IEEE Press, New York (2012)
Chen, F., Au, W.S.A., Valaee, S., Tan, Z.: Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans. Mob. Comput. 11(12), 1983–1993 (2012)
Chai, J.: Patient positioning system in hospital based on Zigbee. In: International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI), pp. 159–162. IEEE Press, New York (2011)
Zhou, S., Pollard, J.K.: Position measurement using bluetooth. IEEE Trans. Consum. Electron. 52(2), 555–558 (2006)
Ni, L.M., Zhang, D., Souryal, M.R.: RFID based localization and tracking technologies. IEEE Wirel. Commun. 18(2), 45–51 (2011)
Deng, Z., Yu, Y., Yuan, X., Wan, N., Yang, L.: Situation and development tendency of indoor positioning. China Commun. 10(3), 42–55 (2013)
Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)
Lowe, D.G.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision, pp. 1150–1157. IEEE Press, New York (1999)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)
OpenCV. http://opencv.org
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Yeh, CC., Lo, YC., Chang, CC. (2016). Design and Implementation of a Smartphone-Based Positioning System. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_84
Download citation
DOI: https://doi.org/10.1007/978-3-319-42007-3_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42006-6
Online ISBN: 978-3-319-42007-3
eBook Packages: Computer ScienceComputer Science (R0)