Abstract
In this paper, a study on effective exploitation of geometrical features for classifying surveillance objects into a set of pre-defined semantic categories is presented. The geometrical features correspond to object’s motion, spatial location and velocity. The extraction of these features is based on object’s trajectory corresponding to object’s temporal evolution. These geometrical features are used to build a behaviour-based classifier to assign semantic categories to the individual blobs extracted from surveillance videos. The proposed classification framework has been evaluated against conventional object classifiers based on visual features extracted from semantic categories defined on AVSS 2007 surveillance dataset.
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References
McCahill, M., Norris, C.: Estimating the extent, sophistication and legality of CCTV in London. In: CCTV, pp. 51–66 (2003)
Piciarelli, C., Micheloni, C., Foresti, G.: Trajectory-based anomalous event detection. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1544–1554 (2008)
Schiele, B., Crowley, J.: Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision 36(1), 31–50 (2000)
Pontil, M., Verri, A.: Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(6), 637–646 (1998)
Bashir, F., Khokhar, A., Schonfeld, D.: Real-time motion trajectory-based indexing and retrieval of video sequences. IEEE Transactions on Multimedia 9(1), 58–65 (2007)
Javed, O., Shah, M.: Tracking and Object Classification for Automated Surveillance. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 343–357. Springer, Heidelberg (2002)
Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
Fernandez Arguedas, V., Zhang, Q., Chandramouli, K., Izquierdo, E.: Multi-feature fusion for surveillance video indexing. In: 12th International Workshop on Image Analysis for Multimedia Interactive Services (April 2011)
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Fernandez Arguedas, V., Chandramouli, K., Izquierdo, E. (2012). Behaviour-Based Object Classifier for Surveillance Videos. In: Moschitti, A., Scandariato, R. (eds) Eternal Systems. EternalS 2011. Communications in Computer and Information Science, vol 255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28033-7_10
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DOI: https://doi.org/10.1007/978-3-642-28033-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28032-0
Online ISBN: 978-3-642-28033-7
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