Abstract
A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. The trajectories are treated as motion time series and modelled using orthogonal basis polynomial approximations. Trajectory clustering is then carried out to discover patterns of similar object motion behaviour. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Clustering in the basis coefficient space leads to efficiency gains over existing approaches that encode trajectories as point-based flow vectors. Experiments on pedestrian motion data gathered from video surveillance demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.
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References
Chang, S.-F., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: “A fully automated content-based video search engine supporting spatiotemporal queries. IEEE Trans. Circuits Syst. Video Technol. 8(5), 602–615 (1998)
Jeannin, S., Divakaran, A.: MPEG-7 visual motion descriptors. IEEE Trans. Circuits Syst. Video Technol. 11(6), 720–724 (2001)
Dagtas, S., Ali-Khatib, W., Ghafor, A., Kashyap, R.L.: Models for motion-based video indexing and retrieval. IEEE Trans. Image Proc. 9(1), 88–101 (2000)
Aghbari, Z., Kaneko, K., Makinouchi, A.: Content-trajectory approach for searching video databases. IEEE Trans. Multimedia 5(4), 516–531 (2003)
Bashir, F., Khokhar, A., Schonfeld, D.: Segmented trajectory-based indexing and retrieval of video data. In: Proc. IEEE Int. Conf. Image Processing, Spain, pp. 623–626 (2003)
Hsu, C.-T., Teng, S.-J.: Motion trajectory based video indexing and retrieval. Proc. IEEE Int. Conf. Image Processing 1, 605–608 (2002)
Bashir, F., Khokhar, A., Schonfeld, D.: A hybrid system for affine-invariant trajectory retrieval. In: Proc. MIR 2004, pp. 235–242 (2004)
Shim, C., Chang, J.: Content-based retrieval using trajectories of moving objects in video databases. In: Proc. IEEE. 7th Int. Conf. Database Systems for Advanced Applications, pp. 169–170 (2001)
Shim, C., Chang, J.: Trajectory-based video retrieval for multimedia information systems. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 372–382. Springer, Heidelberg (2004)
Jin, Y., Mokhtarian, F.: Efficient video retrieval by motion trajectory. In: Proc. BMVC 2004 (2004)
Khalid, S., Naftel, A.: Evaluation of matching metrics for trajectory-based indexing and retrieval of video clips. In: Proc. IEEE WACV, Colorado, USA (January 2004)
Wang, L., Hu, W., Tan, T.: Recent developments in human motion analysis. Pattern Recognition 36(3), 585–601 (2003)
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Systems, Man & Cybernetic, Part C 34(3), 334–352 (2004)
Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. Image Vis. Comput. 14(8), 609–615 (1996)
Owens, J., Hunter, A.: Application of the self-organising map to trajectory classification. In: Proc. IEEE Int. Workshop Visual Surveillance, pp. 77–83 (2000)
Hu, W., Xiao, X., Xie, D., Tan, T., Maybank, S.: Traffic accident prediction using 3-D model-based vehicle tracking. IEEE Trans. Vehicular Tech. 53(3), 677–694 (2004)
Alon, J., Sclaroff, S., Kollios, G., Pavlovic, V.: Discovering clusters in motion time-series data. In: Proc. IEEE CVPR (June 2004)
Hu, W., Xie, D., Tan, T., Maybank, S.: Learning activity patterns using fuzzy self-organizing neural networks. IEEE Trans. Systems, Man & Cybernetic, Pt. B 34(3), 1618–1626 (2004)
Faloutsas, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proc. ACM SIGMOD Conf., pp. 419–429 (1994)
Chan, K., Fu, A.: Efficient time series matching by wavelets. In: Proc. Int. Conf. Data Engineering, Sydney, March 1999, pp. 126–133 (1999)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrota, S.: Locally adaptive dimensionality reduction for indexing large time series databases. In: Proc. ACM SIGMOD Conf., pp. 151–162 (2001)
Cui, Y., Ng, R.: Indexing spatio-temporal trajectories with Chebyshev polynomials. In: Proc. ACM SIGMOD Conf., Paris, June 2004, pp. 599–610 (2004)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)
Kohonen, T.: Self-Organizing Maps, 2nd edn., vol. 30. Springer, New York (1997)
Melo, J., Naftel, A., Bernardino, A., Santos-Victor, J.: Viewpoint independent detection of vehicle trajectories and lane geometry from uncalibrated traffic surveilllance cameras. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 454–462. Springer, Heidelberg (2004)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1998)
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Khalid, S., Naftel, A. (2006). Motion Trajectory Clustering for Video Retrieval Using Spatio-temporal Approximations. In: Bres, S., Laurini, R. (eds) Visual Information and Information Systems. VISUAL 2005. Lecture Notes in Computer Science, vol 3736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590064_6
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DOI: https://doi.org/10.1007/11590064_6
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