Abstract
This paper proposes an approach for trajectory matching in video retrieval. Algorithm is consist of three parts. First, a terse trajectory contains most important temporal and spatial characters are abstracted. Then, abstracted trajectories are classified into several classes by using reformed k-means cluster method according to their position and acceleration features. At the end, Gaussian Process regression model is built and trained using clustered trajectories and trajectories classes which are similar with the given retrieval targets are found out. Advantages of this algorithm include the possibility of generalized clustering for similar trajectories in different scales and partial trajectories matching.
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References
Li, X., Hu, W., Hu, W.: A coarse-to-fine strategy for vehicle motion trajectory clustering. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 1, pp. 591–594. IEEE (2006)
Ossama, O., Mokhtar, H.M.O., El-Sharkawi, M.E.: Clustering Moving Object Trajectories Using Coresets. In: Al-Majeed, S.S., Hu, C.-L., Nagamalai, D. (eds.) WiMoA 2011 and ICCSEA 2011. CCIS, vol. 154, pp. 221–233. Springer, Heidelberg (2011)
Kim, K., Lee, D., Essa, I.: Gaussian process regression flow for analysis of motion trajectories. In: 2011 IEEE International Conference on Computer Vision, ICCV, pp. 1164–1171. IEEE (2011)
Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews (99), 1–23 (2011)
Broilo, M., Piotto, N., Boato, G., Conci, N., De Natale, F.G.B.: Object Trajectory Analysis in Video Indexing and Retrieval Applications. In: Schonfeld, D., Shan, C., Tao, D., Wang, L. (eds.) Video Search and Mining. SCI, vol. 287, pp. 3–32. Springer, Heidelberg (2010)
Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 312–319. IEEE (2009)
Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: 18th International Conference on Pattern Recognition, ICPR 2006, pp. 1135–1138. IEEE (2006)
Hu, R., Barnard, M., Collomosse, J.: Motion-sketch based video retrieval using a trellis levenshtein distance (2010)
Hu, R., James, S., Collomosse, J.: Annotated Free-Hand Sketches for Video Retrieval Using Object Semantics and Motion. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 473–484. Springer, Heidelberg (2012)
Le Thi, L., Boucher, A., Thonnat, M.: Trajectory-based video indexing and retrieval enabling relevance feedback (2010)
Williams, C., Barber, D.: Bayesian classification with gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1342–1351 (1998)
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Wang, T., Zhang, X. (2013). Trajectory Matching Algorithm Based on Clustering and GPR in Video Retrieval. In: Li, K., Li, S., Li, D., Niu, Q. (eds) Intelligent Computing for Sustainable Energy and Environment. ICSEE 2012. Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37105-9_41
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DOI: https://doi.org/10.1007/978-3-642-37105-9_41
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