Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Assessment of a smartphone-based monitoring system and its application

  • Ahn, Hoyong (Department of Spatial Information Engineering, Pukyoung National University) ;
  • Choi, Chuluong (Department of Spatial Information Engineering, Pukyoung National University) ;
  • Yu, Yeon (Spatial Information Institute, Pukyoung National University)
  • Received : 2014.06.20
  • Accepted : 2014.06.25
  • Published : 2014.06.30
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Abstract

Information technology advances are allowing conventional surveillance systems to be combined with mobile communication technologies, creating ubiquitous monitoring systems. This paper proposes monitoring system that uses smart camera technology. We discuss the dependence of interior orientation parameters on calibration target sheets and compare the accuracy of a three-dimensional monitoring system with camera location calculated by space resectioning using a Digital Surface Model (DSM) generated from stereo images. A monitoring housing is designed to protect a camera from various weather conditions and to provide the camera for power generated from solar panel. A smart camera is installed in the monitoring housing. The smart camera is operated and controlled through an Android application. At last the accuracy of a three-dimensional monitoring system is evaluated using a DSM. The proposed system was then tested against a DSM created from ground control points determined by Global Positioning Systems (GPSs) and light detection and ranging data. The standard deviation of the differences between DSMs are less than 0.12 m. Therefore the monitoring system is appropriate for extracting the information of objects' position and deformation as well as monitoring them. Through incorporation of components, such as camera housing, a solar power supply, the smart camera the system can be used as a ubiquitous monitoring system.

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References

  1. Akca, D., and A. Gruen, 2009. Comparative geometric and radiometric evaluation of mobile phone and still video cameras, Photogramm. Rec., 24: 217-245. https://doi.org/10.1111/j.1477-9730.2009.00541.x
  2. Botan, C. and M. Vorvoreanu, 2005. What do employees think about electronic surveillance at work?, Electronic Monitoring in the Workplace: Controversies and Solutions, Idea Group Publishing.
  3. Brown, D., 1971. Close-range Camera calibration, PE & RS, 37: 855-866.
  4. Cas, J., 2005. Privacy in pervasive computing environments - a contradiction in terms, IEEE Technology and Society Magazine, 24(1): 24-33. https://doi.org/10.1109/MTAS.2005.1407744
  5. Chandler, J. H., J.G. Fryer, and A. Jack, 2005. Metric capabilities of low-cost digital cameras for close range surface measurement, Photogramm. Rec., 20: 12-26. https://doi.org/10.1111/j.1477-9730.2005.00302.x
  6. Chang, Y., C. Chen, and S. Zhou, 2009. Smart phone for mobile commerce, Computer Standards & Interfaces, 31: 740-747. https://doi.org/10.1016/j.csi.2008.09.016
  7. Coroama, V., J. Bohn, and F. Mattern, 2004. Living in a smart environment - implications for the coming ubiquitous information society, IEEE international Conference on Systems, Man and Cybernetics, 6: 5633-5638.
  8. D'Amelio, S., and M.L. Brutto, 2009. Close range photogrammetry for measurement of paintings surface deformations, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5/W1): 6.
  9. Erdas, Intergraph corporate website, 2011.
  10. Fleck, S. and W. Strasser, 2008. Smart Camera Based Monitoring System and Its Application to Assisted Living, Proc. of the IEEE, Vol.96 (10), pp. 1698-1714. https://doi.org/10.1109/JPROC.2008.928765
  11. Fraser, C.S., 2012. Automatic Camera calibration in close-range photogrammetry, Photogrammetric Engineering & Remote Sensing, 79(4): 381-388. https://doi.org/10.14358/PERS.79.4.381
  12. Fraser, C.S., 1997. Digital camera self-calibration. ISPRS Journal of Photogrammetry and Remote Sensing, 52(4): 149-159. https://doi.org/10.1016/S0924-2716(97)00005-1
  13. Google, 2012. Android Developers websites. Android ver. 4.1.2.
  14. Goshtasby, A., 1989. Correction of image deformation from lens distortion using Bezier patches, Computer vision, Graphics, and Image Processing, 47(3): 358-394.
  15. Hinz, S., M. Stephani, L. Schiemann, and K. Zeller, 2009. An image engineering system for the inspection of transparent construction materials, ISPRS J. Photogramm. Remote Sens., 64: 297-307. https://doi.org/10.1016/j.isprsjprs.2008.10.006
  16. Joundi, R.A., J.S. Brittain, N. Jenkinson, A.L. Green, and T. Aziz, 2011. Rapid tremor frequency assessment with the iPhone accelerometer, Parkinsonism and Related Disorders, 17(4): 288-290. https://doi.org/10.1016/j.parkreldis.2011.01.001
  17. Kim, J., S. Lee, H. Ahn, D. Seo, J. Lee, and C. Choi, 2013a. Accuracy evaluation of a smartphone-based technology for coastal monitoring, Measurement, 46(1): 233-248. https://doi.org/10.1016/j.measurement.2012.06.010
  18. Kim, J., S. Lee, H. Ahn, D. Seo, S. Park, and C. Choi, 2013b. Feasibility of employing a smartphone as the payload in a photogrammetric UAV system, ISPRS Journal of Photogrammetry and Remote Sensing, 79: 1-18. https://doi.org/10.1016/j.isprsjprs.2013.02.001
  19. Lee S, J. Kim, C. Jin, S. Bae, and C. Choi, 2012a. Assessment of smartphone based technology for remote environmental monitoring and its development, Instrumentation Science & Technology, 40(6): 504-529. https://doi.org/10.1080/10739149.2012.700534
  20. Lee S, C. Jin, J. Kim, and C. Choi,2012b. Development of a smartphone application for environmental monitoring, Proc. of the ASPRS Annual Conference, Sacramento California, pp. 19-23.
  21. Monserrat, O. and M. Crosetto, 2008. Deformation measurement using terrestrial laser scanning data and least squares 3D surface matching, ISPRS Journal of Photogrammetry and Remote Sensing, 63: 142-154. https://doi.org/10.1016/j.isprsjprs.2007.07.008
  22. Moran, S. and K. Nakata, 2010. Analyzing the Factors Affecting Users in Intelligent Pervasive Spaces, Intelligent Buildings International, 2(1): 57-71. https://doi.org/10.3763/inbi.2009.0040
  23. Pedersini, F., A. Sarti, and S. Tubaro, 1999. Accurate and simple geometric calibration of multi-camera systems, Signal Process., 77: 309-334. https://doi.org/10.1016/S0165-1684(99)00042-0
  24. Ricolfe-Viala, C., and A. Sanchez-Salmeron, 2011. Using the camera pin-hole model restrictions to calibrate the lens distortion model, Opt. Laser Technol., 43: 996-1005. https://doi.org/10.1016/j.optlastec.2011.01.006
  25. Rieke-Zapp, D.H., and M.A. Nearing, 2005. Digital close range photogrammetry for measurement of soil erosion, Photogramm. Rec., 20: 69-87. https://doi.org/10.1111/j.1477-9730.2005.00305.x
  26. Singh, S., S. Puradkar, and Y. Lee, 2006. Ubiquitous computing: connecting pervasive computing through Semantic Web, Information Systems and E-Business Management, 4(4): 421-439. https://doi.org/10.1007/s10257-005-0003-8
  27. Suziedelyte-Visockiene, J., 2012. Photogrammetry requirements for digital Camera calibration applying Tcc and MatLab software, Geodesy and Cartography, 38(3): 106-110. https://doi.org/10.3846/20296991.2012.728895
  28. Tessens, L., M. Morbee, H. Aghajan, and W. Philips, 2014. Camera Selection for Tracking in Distributed Smart Camera Networks, ACM Transactions on Sensor Networks, 10(2): 23.
  29. Wolf, P.R., and B.A. Dewitt, 2000. Elements of photogrammetry with Application in GIS, McGraw-Hill Book Company, New York.