Abstract
Scene understanding corresponds to the real time process of perceiving, analysing and elaborating an interpretation of a 3D dynamic scene observed through a network of cameras. The whole challenge consists in managing this huge amount of information and in structuring all the knowledge. On-line Clustering is an efficient manner to process such huge amounts of data. On-line processing is indeed an important capability required to perform monitoring and behaviour analysis on a long-term basis. In this paper we show how a simple clustering algorithm can be tuned to perform on-line. The system works by finding the main trajectory patterns of people in the video. We present results obtained on real videos corresponding to the monitoring of the Toulouse airport in France.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cofriend, http://easaier.silogic.fr/co-friend/ , http://easaier.silogic.fr/co-friend/
Anjum, N., Cavallaro, A.: Single camera calibration for trajectory-based behavior analysi. In: IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 147–152. IEEE, Los Alamitos (2007)
Antonini, G., Thiran, J.: Counting pedestrians in video sequences using trajectory clustering. IEEE Transactions on Circuits and Systems for Video Technology 16, 1008–1020 (2006)
Avanzi, A., Bremond, F., Tornieri, C., Thonnat, M.: Design and assessment of an intelligent activity monitoring platform. EURASIP Journal on Advances in Signal Processing 2005, 2359–2374 (2005)
Bashir, F., Khokhar, A., Schonfeld, D.: Object trajectory-based activity classification and recognition using hidden markov models. IEEE Transactions on Image Processing 16, 1912–1919 (2007)
Campello, R., Hruschka, E.: A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets and Systems 157, 2858–2875 (2006)
Carpenter, G.A., Grossberg, S.: A self-organizing neural network for supervised learning, recognition, and prediction. IEEE Communications Magazine 30, 38–49 (1992)
Carpenter, G.A., Grossberg, S., Reynolds, J.: Artmap: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks 4, 565–588 (1991)
Davies, D., Bouldin, D.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)
Dunn, J.: Well-separated clusters and optimal fuzzy partitions. Cybernetics and Systems 4, 95–104 (1974)
Foresti, G., Micheloni, C., Snidaro, L.: Event classification for automatic visual-based surveillance of parking lots. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 314–317. IEEE, Los Alamitos (2004)
Fusier, F., Valentin, V., Bremond, F., Thonnat, M., Borg, M., Thirde, D., Ferryman, J.: Video understanding for complex activity recognition. Machine Vision and Applications 18, 167–188 (2007)
Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, United States (1999)
Hartigan, J.A.: Clustering algorithms. John Wiley & Sons, Inc., New York (1975)
Jantke, K.: Types of incremental learning. In: AAAI Symposium on Training Issues in Incremental Learning, pp. 23–25 (1993)
Kaufman, L., Rousseeuw, P.: Finding groups in data. An introduction to cluster analysis, New York (1990)
Lange, S., Grieser, G.: On the power of incremental learning. Theoretical Computer Science 288, 277–307 (2002)
Livny, M., Zhang, T., Ramakrishnan, R.: Birch: an efficient data clustering method for very large databases. In: ACM SIGMOD International Conference on Management of Data, Montreal, vol. 1, pp. 103–114 (1996)
Muhlbaier, M.D., Topalis, A., Polikar, R.: Learn++.nc: Combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes. IEEE Transactions on Neural Networks 20, 152–168 (2009)
Naftel, A., Khalid, S.: Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space. Multimedia Systems 12, 227–238 (2006)
Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 831–843 (2000)
Piciarelli, C., Foresti, G., Snidaro, L.: Trajectory clustering and its applications for video surveillance. In: Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, 2005, pp. 40–45. IEEE, Los Alamitos (2005)
Polikar, R., Upda, L., Upda, S., Honavar, V.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 31, 497–508 (2001)
Porikli, F.: Learning object trajectory patterns by spectral clustering. In: 2004 IEEE International Conference on Multimedia and Expo (ICME), vol. 2, pp. 1171–1174. IEEE, Los Alamitos (2004)
Seipone, T., Bullinaria, J.: Evolving neural networks that suffer minimal catastrophic forgetting. In: Modeling Language, Cognition and Action - Proceedings of the Ninth Neural Computation and Psychology Workshop, pp. 385–390. World Scientific Publishing Co. Pte. Ltd., Singapore (2005)
Sharma, A.: A note on batch and incremental learnability. Journal of Computer and System Sciences 56, 272–276 (1998)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2005)
Vu, V.-T., Brémond, F., Thonnat, M.: Automatic video interpretation: A recognition algorithm for temporal scenarios based on pre-compiled scenario models. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 523–533. Springer, Heidelberg (2003)
Vijaya, P.: Leaders subleaders: An efficient hierarchical clustering algorithm for large data sets. Pattern Recognition Letters 25, 505–513 (2004)
Zhou, Z., Chen, Z.: Hybrid decision tree. Knowledge-based systems 15, 515–528 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Patino, L., Bremond, F., Thonnat, M. (2011). Incremental Learning on Trajectory Clustering. In: Remagnino, P., Monekosso, D.N., Jain, L.C. (eds) Innovations in Defence Support Systems – 3. Studies in Computational Intelligence, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18278-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-18278-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18277-8
Online ISBN: 978-3-642-18278-5
eBook Packages: EngineeringEngineering (R0)