Visual Navigation with Street View Image Matching

Chih-Hung Hsu, Huei-Yung Lin

Abstract

The vision based navigation approach is a key to success for the driving assistance technology. In this work, we presents a visual navigation assistance system based on the geographic information of the vehicle and image matching between the online and pre-established data. With the rough GPS coordinates, we utilize the image retrieval algorithms to find the most similar image in the panoramic image database. The searching results are then compared with the input image for feature matching to find the landmarks in the panoramic image. By using the 360 field-of-view of the panoramic images, the camera’s heading can be calculated by the matching results. Finally, the landmark information is identified by the markers on the Google map as visual guidance and assistance.

References

  1. Altwaijry, H., Moghimi, M., and Belongie, S. (2014). Recognizing locations with google glass: A case study. In Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, pages 167-174.
  2. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). Speeded-up robust features (surf). Comput. Vis. Image Underst., 110(3):346-359.
  3. Bettadapura, V., Essa, I. A., and Pantofaru, C. (2015). Egocentric field-of-view localization using first-person point-of-view devices. In 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, Waikoloa, HI, USA, January 5-9, 2015, pages 626-633. IEEE Computer Society.
  4. Canny, J. (1986). A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679-698.
  5. Chen, D. M., Baatz, G., Koser, K., Tsai, S. S., Vedantham, R., Pylvanainen, T., Roimela, K., Chen, X., Bach, J., Pollefeys, M., Girod, B., and Grzeszczuk, R. (2011). City-scale landmark identification on mobile devices. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 7811, pages 737-744, Washington, DC, USA. IEEE Computer Society.
  6. Guan, T., Fan, Y., Duan, L., and Yu, J. (2014). On-device mobile visual location recognition by using panoramic images and compressed sensing based visual descriptors.
  7. Hough, P. V. (1962). Method and means for recognizing complex patterns. US Patent 3,069,654.
  8. Hu, R. and Collomosse, J. (2013). A performance evaluation of gradient field hog descriptor for sketch based image retrieval. Comput. Vis. Image Underst., 117(7):790-806.
  9. Huang, W.-T., Tsai, C.-L., and Lin, H.-Y. (2012). Mobile robot localization using ceiling landmarks and images captured from an rgb-d camera. In Advanced Intelligent Mechatronics (AIM), 2012 IEEE/ASME International Conference on, pages 855-860.
  10. Hughes, J. F., van Dam, A., McGuire, M., Sklar, D. F., Foley, J. D., Feiner, S. K., and Akeley, K. (2013). Computer graphics: principles and practice (3rd ed.). Addison-Wesley Professional, Boston, MA, USA.
  11. Juan, L. and Gwun, O. (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing (IJIP), 3(4):143-152.
  12. Laungrungthip, N., McKinnon, A., Churcher, C., and Unsworth, K. (2008). Edge-based detection of sky regions in images for solar exposure prediction. In Image and Vision Computing New Zealand, 2008. IVCNZ 2008. 23rd International Conference, pages 1- 6.
  13. Lin, H., Lin, Y., and Yao, J. (2013). Scene change detection and topological map construction using omnidirectional image sequences. In Proceedings of the 13. IAPR International Conference on Machine Vision Applications, MVA 2013, Kyoto, Japan, May 20-23, 2013, pages 57-60.
  14. Liu, H., Mei, T., Luo, J., Li, H., and Li, S. (2012). Finding perfect rendezvous on the go: Accurate mobile visual localization and its applications to routing. In Proceedings of the 20th ACM International Conference on Multimedia, MM 7812, pages 9-18, New York, NY, USA. ACM.
  15. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vision, 60(2):91- 110.
  16. Mulloni, A., Wagner, D., Barakonyi, I., and Schmalstieg, D. (2009). Indoor positioning and navigation with camera phones. Pervasive Computing, IEEE, 8(2):22-31.
  17. Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8.
  18. Resch, B., Lang, J., and Lensch, H. (2014). Local image feature matching improvements for omnidirectional camera systems. In Pattern Recognition (ICPR), 2014 22nd International Conference on, pages 918-923.
  19. Saab, S. and Nakad, Z. (2011). A standalone rfid indoor positioning system using passive tags. Industrial Electronics, IEEE Transactions on, 58(5):1961-1970.
  20. Siddiqui, J. and Khatibi, S. (2014). Semantic urban maps. In Pattern Recognition (ICPR), 2014 22nd International Conference on, pages 4050-4055.
  21. Turcot, P. and Lowe, D. (2009). Better matching with fewer features: The selection of useful features in large database recognition problems. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pages 2109-2116.
  22. Uchiyama, H., Saito, H., Servieres, M., and Moreau, G. (2009). Image based view localization system retrieving from a panorama database by SURF. In Proceedings of the IAPR Conference on Machine Vision Applications (IAPR MVA 2009), Keio University, Yokohama, Japan, May 20-22, 2009, pages 118-121.
  23. Wang, S., Wang, Y., and Zhu, S.-C. (2015). Learning hierarchical space tiling for scene modeling, parsing and attribute tagging. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PP(99):1-1.
  24. Yao, C.-W., Cheng, K.-S., and Lin, H.-Y. (2014). A vision assisted vehicle navigation technique based on topological map construction and scene recognition. In Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on, pages 399-404.
  25. Zamir, A. R. and Shah, M. (2010). Accurate image localization based on google maps street view. In Proceedings of the 11th European Conference on Computer Vision: Part IV, ECCV'10, pages 255-268, Berlin, Heidelberg. Springer-Verlag.
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Paper Citation


in Harvard Style

Hsu C. and Lin H. (2016). Visual Navigation with Street View Image Matching . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 583-589. DOI: 10.5220/0005674405830589


in Bibtex Style

@conference{visapp16,
author={Chih-Hung Hsu and Huei-Yung Lin},
title={Visual Navigation with Street View Image Matching},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={583-589},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005674405830589},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Visual Navigation with Street View Image Matching
SN - 978-989-758-175-5
AU - Hsu C.
AU - Lin H.
PY - 2016
SP - 583
EP - 589
DO - 10.5220/0005674405830589