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Accurate Image Localization Based on Google Maps Street View

  • Amir Roshan Zamir
  • Mubarak Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

Finding an image’s exact GPS location is a challenging computer vision problem that has many real-world applications. In this paper, we address the problem of finding the GPS location of images with an accuracy which is comparable to hand-held GPS devices.We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree. In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors. Then, a smoothing step with an associated voting scheme is utilized; this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A parameter called Confidence of Localization which is based on the Kurtosis of the distribution of votes is defined to determine how reliable the localization of a particular image is. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. First, each image is localized individually; then, the rest of the images in the group are matched against images in the neighboring area of the found first match. The final location is determined based on the Confidence of Localization parameter. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.

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References

  1. 1.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building rome in a day. In: ICCV (2009)Google Scholar
  2. 2.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25, 835–846 (2006)CrossRefGoogle Scholar
  3. 3.
    Jacobs, N., Satkin, S., Roman, N., Speyer, R., Pless, R.: Geolocating static cameras. In: ICCV (2007)Google Scholar
  4. 4.
    Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR, pp. 1–7 (2007)Google Scholar
  5. 5.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of the, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, vol. 2, pp. 2161–2168. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  6. 6.
    Crandall, D., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: International World Wide Web Conference (2009)Google Scholar
  7. 7.
    Kalogerakis, E., Vesselova, O., Hays, J., Efros, A., Hertzmann, A.: Image sequence geolocation with human travel priors. In: ICCV (2009)Google Scholar
  8. 8.
    Zhang, W., Kosecka, J.: Image based localization in urban environments. In: 3DPVT 2006: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT 2006), pp. 33–40 (2006)Google Scholar
  9. 9.
    Hakeem, A., Vezzani, R., Shah, M., Cucchiara, R.: Estimating geospatial trajectory of a moving camera. In: International Conference on Pattern Recognition, vol. 2, pp. 82–87 (2006)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60 (2004)Google Scholar
  11. 11.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
  12. 12.
    Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: VISAPP (2009)Google Scholar
  13. 13.
    Balanda, K.P., MacGillivray, H.L.: Kurtosis: A critical review. The American Statistician 42, 111–119 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Amir Roshan Zamir
    • 1
  • Mubarak Shah
    • 1
  1. 1.University of Central FloridaOrlandoUSA

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