Interactions of dynamic geospatial objects with static landmarks

• Autorzy: Czejdo B. , Bhattacharya S. , Malhotra R.
• Języki publikacji: EN

Abstrakty

EN

Analyzing the behavior of moving objects has multitude of applications e.g. in the area of transportation. Each application might require identification of different behavior patterns and their relationships to different landmarks. Machine learning algorithms can help in automatic recognition of spatiotemporal patterns. However this is still a largely unsolved problem, especially identification of the relationships of moving point objects with stationary objects or landmarks on a map. In our project we considered dynamic objects such as cars and humans on a terrain with static elements such as road networks and buildings e.g. airports, bus stops etc. We created application specific ontologies of patterns of moving objects in relation to static landmarks. Based on ontologies we built machine learning models to classify trajectories of moving objects.

Słowa kluczowe

EN

dynamic objects; geospatial objects; static landmarks; movement patterns

PL

obiekty dynamiczne; obiekty geoprzestrzenne; statyczne elementy krajobrazu; modele ruchu

• Wydawca: Instytut Naukowo-Wydawniczy "TTS" Sp. z o.o
• Czasopismo: TTS Technika Transportu Szynowego
• Rocznik: 2013
• Tom: R. 20, nr 10
• Strony: 2255--2264, CD
• Opis fizyczny: Bibliogr. 15 poz., il. kolor., 1 wykr.

Twórcy

autor

Czejdo B.

• Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, NC, USA

autor

Bhattacharya S.

• Department of Mathematics and Computer Science, Fayetteville State University, NC, USA

autor

Malhotra R.

• Department of Government and History, Fayetteville State University, NC, USA

Bibliografia

• 1. Long, J. A. and T. A. Nelson (2012). "A review of quantitative methods for movement data." International Journal of Geographical Information Science: 1-27.
• 2. Dodge, S., R. Weibel, et al. (2008). "Towards a taxonomy of movement patterns." Information visualization 7(3-4): 240-252.
• 3. Percivall, G. (2012). Connecting islands in the internet of things. Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications. Washington, D.C., ACM: 1-1.
• 4. Platt, J. (1999). "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods." Advances in large margin classifiers 10(3): 61-74.
• 5. Vapnik, V. (1999). The nature of statistical learning theory, Springer.
• 6. Joachims, T. (1999). "Making large scale SVM learning practical."
• 7. Johnson, N. and D. Hogg (1996). "Learning the distribution of object trajectories for event recognition." Image and Vision Computing 14(8): 609-615.
• 8. Weinland, D., R. Ronfard, et al. (2011). "A survey of vision-based methods for action representation, segmentation and recognition." Computer Vision and Image Understanding 115(2): 224-241.
• 9. Chih-Wei Hsu, C.-C. C., and Chih-Jen Lin. "A Practical Guide to Support Vector Classification." Retrieved June 29, 2012, from http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
• 10. C.-C. Chang and C.-J. Lin. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
• 11. Ben-Hur, A. and J. Weston (2010). "A User’s Guide to Support Vector Machines Data Mining Techniques for the Life Sciences." 609: 223-239.
• 12. T. F. Wu, C. J. L., and R. C. Weng. (2004). "Probability estimates for multi-class classification by pairwise coupling." Journal of Machine Learning Research 5: 975-1005.
• 13. NetLogo URL. http://ccl.northwestern.edu/netlogo/
• 14. ArcGIS URL. http://www.esri.com/software/arcgis
• 15. Murphy, K. P. (2012). "Machine Learning: a Probabilistic Perspective."