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GPSView: A scenic driving route planner

Published:19 February 2013Publication History
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Abstract

GPS devices have been widely used in automobiles to compute navigation routes to destinations. The generated driving route targets the minimal traveling distance, but neglects the sightseeing experience of the route. In this study, we propose an augmented GPS navigation system, GPSView, to incorporate a scenic factor into the routing. The goal of GPSView is to plan a driving route with scenery and sightseeing qualities, and therefore allow travelers to enjoy sightseeing on the drive. To do so, we first build a database of scenic roadways with vistas of landscapes and sights along the roadside. Specifically, we adapt an attention-based approach to exploit community-contributed GPS-tagged photos on the Internet to discover scenic roadways. The premise is: a multitude of photos taken along a roadway imply that this roadway is probably appealing and catches the public's attention. By analyzing the geospatial distribution of photos, the proposed approach discovers the roadside sight spots, or Points-Of-Interest (POIs), which have good scenic qualities and visibility to travelers on the roadway. Finally, we formulate scenic driving route planning as an optimization task towards the best trade-off between sightseeing experience and traveling distance. Testing in the northern California area shows that the proposed system can deliver promising results.

References

  1. Agarwal, S., Snavely, N., Simon, I., Seitz, S. M., and Szeliski, R. 2009. Building rome in a day. In Proceedings of International Conference on Computer Vision.Google ScholarGoogle Scholar
  2. Asakura, Y. and Iryo, T. 2007. Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument. Transp. Res. A: Policy Pract. 41, 7, 684--690.Google ScholarGoogle ScholarCross RefCross Ref
  3. Bellman, R. 1958. On a routing problem. Quart. Appl. Math. 16, 87--90.Google ScholarGoogle ScholarCross RefCross Ref
  4. Chippendale, P., Zanin, M., and Andreatta, C. 2009. Collective photography. In Proceedings of the Conference for Visual Media Production 0, 188--194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. 2001. Introduction to Algorithms. MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Crandall, D. J., Backstrom, L., Huttenlocher, D., and Kleinberg, J. 2009. Mapping the world's photos. In Proceedings of the 18th International Conference on World Wide Web. ACM, New York, 761--770. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., and Yu, C. 2010. Automatic construction of travel itineraries using social breadcrumbs. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT '10). ACM, New York, 35--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Dijkstra, E. W. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 269--271.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Elias, B. and Sester, M. 2006. Incorporating landmarks with quality measures in routing procedures. In Proceedings of the International Conference on Geographic Information Science. 65--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ester, M., Kriegel, H.-P., Jörg, S., and Xu, X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Conference on Knowledge Discovery and Data Mining. ACM, 226--231.Google ScholarGoogle Scholar
  11. Goesele, M., Snavely, N., Curless, B., Hoppe, H., and Seitz, S. M. 2007. Multi-View stereo for community photo collections. In Proceedings of the IEEE Conference on Computer Vision.Google ScholarGoogle Scholar
  12. Hao, Q., Cai, R., Wang, C., Xiao, R., Yang, J.-M., Pang, Y., and Zhang, L. 2010. Equip tourists with knowledge mined from travelogues. In Proceedings of the 19th International Conference on World Wide Web (WWW '10). ACM, New York, 401--410. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hao, Q., Cai, R., Wang, X.-J., Yang, J.-M., Pang, Y., and Zhang, L. 2009. Generating location overviews with images and tags by mining user-generated travelogues. In Proceedings of the 17th ACM International Conference on Multimedia. ACM, New York, 801--804. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hochmair, H. and Navratil, G. 2008. Computation of scenic routes in street networks. In Proceedings of the Geoinformatics Forum Salzburg.Google ScholarGoogle Scholar
  15. Hochmair, H. H. 2007. Optimal route selection with route planners: Results of a desktop usability study. In Proceedings of the Workshop on Advances in Geographic Information Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jesdanun, A. 2008. Gps adds dimension to online photos citation. http://www.physorg.com/news119889687.html.Google ScholarGoogle Scholar
  17. Jing, F., Zhang, L., and Ma, W.-Y. 2006. Virtualtour: An online travel assistant based on high quality images. In Proceedings of the 14th Annual ACM International Conference on Multimedia. ACM, New York, 599--602. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Joliffe, I. T. 1986. Principal Component Analysis. Springer-Verlag.Google ScholarGoogle Scholar
  19. Kalogerakis, E., Vesselova, O., Hays, J., Efros, A. A., and Hertzmann, A. 2009. Image sequence geolocation with human travel priors. In Proceedings of the IEEE International Conference on Computer Vision (ICCV '09).Google ScholarGoogle Scholar
  20. Kawai, Y., Zhang, J., and Kawasaki, H. 2009. Tour recommendation system based on web information and gis. In Proceedings of the IEEE International Conference on Multimedia and Expo. 990--993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Kennedy, L., Naaman, M., Ahern, S., Nair, R., and Rattenbury, T. 2007. How flickr helps us make sense of the world: context and content in community-contributed media collections. In Proceedings of the International Conference on Multimedia. 631--640. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Lewa, A. and McKerchera, B. 2006. Modeling tourist movements: A local destination analysis. Ann. Tour. Res. 33, 2, 403--423.Google ScholarGoogle ScholarCross RefCross Ref
  23. Li, X., Wu, C., Zach, C., Lazebnik, S., and Frahm, J.-M. 2008. Modeling and recognition of landmark image collections using iconic scene graphs. In Proceedngs of the European Conference on Computer Vision. 427--440. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Rattenbury, T., Good, N., and Naaman, M. 2007. Towards automatic extraction of event and place semantics from flickr tags. In Proceedings of ACM SIGIR. ACM, New York, 103--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Rodgers, J. L. and Nicewander, W. A. 1988. Thirteen ways to look at the correlation coefficient. Amer. Statist. 42, 59--66.Google ScholarGoogle ScholarCross RefCross Ref
  26. Sander, J., Ester, M., Kriegel, H.-P., and Xu, X. 1998. Density-Based clustering in spatial databases: The algorithm gdbscan and its applications. Data Min. Knowl. Discov. 2, 2, 169--194. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Snavely, N., Seitz, S. M., and Szeliski, R. 2006. Photo tourism: Exploring photo collections in 3D. ACM Trans. Graph. 835--846. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Snavely, N., Seitz, S. M., and Szeliski, R. 2008. Modeling the world from Internet photo collections. Int. J. Comput. Vis. 80, 2, 189--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Torniai, C., Battle, S., and Cayzer, S. 2007. Sharing, discovering and browsing geotagged pictures on the web. Tech. rep., HP Laboratories Bristol.Google ScholarGoogle Scholar
  30. Winter, S. 2002. Modeling costs of turns in route planning. Geoinformatica 6, 363--380. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Yanai, K., Kawakubo, H., and Qiu, B. 2009. A visual analysis of the relationship between word concepts and geographical locations. In Proceeding of the ACM International Conference on Image and Video Retrieval. ACM, New York, 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Zhang, J., Kawasaki, H., and Kawai, Y. 2008. A tourist route search system based on web information and the visibility of scenic sights. In Proceedings of the International Symposium on Universal Communication. 154--161. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Zheng, Y., Zhang, L., Xie, X., and Ma, W.-Y. 2009a. Mining interesting locations and travel sequences from gps trajectories. In Proceedings of the 18th International Conference on World Wide Web. ACM, New York, 791--800. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Zheng, Y.-T., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.-S., and Neven, H. 2009b. Tour the world: Building a web-scale landmark recognition engine. In Proceedings of the International Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle Scholar

Index Terms

  1. GPSView: A scenic driving route planner

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    Alyx Macfadyen

    Early work on the development of GPSView, an augmented global positioning system (GPS) that returns an optimal scenic driving route, is presented. This is in contrast to commonly used systems for automobiles that provide the shortest route with the least traveling distance. The work in this paper aims to produce both optimal traveling distance and a pleasurable sightseeing experience. For this investigation, the authors built a database of GPS-tagged scenic landscape photos that are freely available online. By analyzing the geospatial distribution of these images in a mapping system, the authors locate "hotspots" or points of interest (POIs). The best drive-by landscapes are chosen and the route is optimized for both traveling distance and best scenic drive. However, Google Maps (http://maps.google.com/), Google Earth (http://www.google.com/earth/), and apps such as the free "Navigon" (http://www.navigon.com) on my Samsung Galaxy device already provide similar types of directions and route finding. Furthermore, existing services allow users to tailor their own scenic routes and peruse tagged images of POIs online. The authors differentiate their system by defining scenic driving as a "sightseeing activity that takes place in automobiles when people are traveling." The aim therefore is to construct a scenic route that presents only geographical landmarks of interest that are visible from a moving vehicle. The algorithmic calculations for mining and selecting tourist imagery, and the evaluation of roadside visibility produce an interesting discussion. The authors conclude the paper by identifying other critical challenges, such as multilane highways and streets containing traffic islands or barriers. They also identify the variations in attractiveness of scenery due to seasonal changes as another area for consideration. This is interesting research that would be a useful and intuitive add-on for existing systems. Anyone working in GPS mapping may find it interesting. However, there is still some way to go with this project. Online Computing Reviews Service

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 9, Issue 1
      February 2013
      158 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/2422956
      Issue’s Table of Contents

      Copyright © 2013 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 19 February 2013
      • Revised: 1 January 2012
      • Accepted: 1 January 2012
      • Received: 1 September 2010
      Published in tomm Volume 9, Issue 1

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