Journals / International Journal of Engineering and Geosciences / 2020 / Cilt: 5 - Sayı: 2

INTEGRATION OF CUSTOM STREET VIEW AND LOW COST MOTION SENSOR

Pages
66–72
Publication Date
2022-10-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed system can be implemented forvirtual museums, heritage sites or planetariums consisting of panoramic images

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Virtual reality is an artificial computer-generated environment generally referred as virtual realityenvironment which can be navigated and interacted with by a user.
📊
Key Findings & Data:Street View, which was released by Google in 2007, isan ideal tool to discover places and locations.
💡
Conclusion & Impact:The proposed system can be implemented forvirtual museums, heritage sites or planetariums consisting of panoramic images
🔬Design:Research Article
📍Scope:2007 Verileri
⚙️Variables:Domain Parameters

Abstract

Virtual reality is an artificial computer-generated environment generally referred as virtual realityenvironment which can be navigated and interacted with by a user. Street View, which was released by Google in 2007, isan ideal tool to discover places and locations. This service doesn’t only provide spatial information, but also a virtual realityenvironment for the user. Since this service is only available in certain locations, Google enables users to create a streetview with custom panoramic images with the help of Google Maps Application Programming Interface (API) for JavaScript.In this study, it is aimed to integrate body motions with a custom created street view service for Yildiz Technical UniversityDavutpasa Campus which has a historical environment and huge places to discover. Microsoft Kinect for Xbox 360 motionsensor along with Flexible Action and Articulated Skeleton Toolkit (FAAST) interface has been employed for this purpose.This integration provides a low-cost alternative for virtual reality experience. The proposed system can be implemented forvirtual museums, heritage sites or planetariums consisting of panoramic images

🔖

Cite This Article

GUMUSAY, M. U., BAKİRMAN, T. (2022). INTEGRATION OF CUSTOM STREET VIEW AND LOW COST MOTION SENSOR. International Journal of Engineering and Geosciences, Cilt: 5 - Sayı: 2, 66–72. https://ajindex.com/articles/540711

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (7)

  1. Akcay, O., Erenoglu, R. C., & Avsar, E. O. (2017). The Effect of JPEG Compression in Close Range Photogrammetry. International Journal of Engineering and Geosciences, 2(1), 35-40. doi: 10.26833/ijeg.287308 Amiri Parian, J., & Gruen, A. (2010). Sensor modeling, self-calibration and accuracy testing of panoramic cameras and laser scanners. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 60-76. doi: https://doi.org/10.1016/j.isprsjprs.2009.08.005
  2. Anguelov, D., Dulong, C., Filip, D., Frueh, C., Lafon, S., Lyon, R.,. Weaver, J. (2010). Google Street View: Capturing the World at Street Level. Computer, 43(6), 32-38. doi: 10.1109/MC.2010.170 Bakirman, T., Gumusay, M. U., Reis, H. C., Selbesoglu, M. O., Yosmaoglu, S., Yaras, M. C.,. Bayram, B. (2017). Comparison of low cost 3D structured light scanners for face modeling. Applied Optics, 56(4), 985- 992. doi: 10.1364/AO.56.000985 Chapman, D., & Deacon, A. (1998). Panoramic imaging and virtual reality — filling the gaps between the lines. ISPRS Journal of Photogrammetry and Remote Sensing, 53(6), 311-319. doi: https://doi.org/10.1016/S0924- 2716(98)00016-1
  3. Curtis, J. W., Curtis, A., Mapes, J., Szell, A. B., & Cinderich, A. (2013). Using google street view for systematic observation of the built environment: analysis of spatio-temporal instability of imagery dates. International Journal of Health Geographics, 12(1), 53. doi: 10.1186/1476-072X-12-53
  4. Fangi, G., & Nardinocchi, C. (2013). Photogrammetric Processing of Spherical Panoramas. The Photogrammetric Record, 28(143), 293-311. doi: 10.1111/phor.12031
  5. Freedman, B., Shpunt, A., Machline, M., & Arieli, Y. (2013). Depth mapping using projected patterns: Google Patents. Google. (2019). Where we’ve been. Retrieved 11.06.2019, from https://www.google.com/streetview/explore/
  6. Gutierrez, M., Vexo, F., & Thalmann, D. (2008). Stepping into virtual reality: Springer Science & Business Media. Hanson, C. S., Noland, R. B., & Brown, C. (2013). The severity of pedestrian crashes: an analysis using Google Street View imagery. Journal of Transport Geography, 33, 42-53. doi:https://doi.org/10.1016/j.jtrangeo.2013.09.002
  7. Isikdag, U. (2020) An IoT Architecture for Facilitating Integration of GeoInformation. International Journal of Engineering and Geosciences, 5(1), 15-25. doi: 10.26833/ijeg.587023 Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P.,. Fitzgibbon, A. (2011). KinectFusion: real-time 3D reconstruction and interaction using a