Abstract
We address the problem of indexing video sequences according to the events they depict. While a number of different approaches have been proposed in order to describe events, none is sufficiently generic and computationally efficient to be applied to event-based retrieval of video sequences within large databases. In this paper, we propose a novel index of video sequences which aims at describing their dynamic content. This index relies on the local feature trajectories estimated from the spatio-temporal volume of the video sequences. The computation of this index is efficient, makes assumption neither about the represented events nor about the video sequences. We show through a batch of experimentations on standard video sequence corpus that this index permits to classify complex human activities as efficiently as state of the art methods while being far more efficient to retrieve generic classes of events.
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References
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)
Bretzner, L., Lindeberg, T.: Feature tracking with automatic selection of spatial scales. Computer Vision and Image Understanding: CVIU 71(3), 385–392 (1998)
Bruno, É., Moënne-Loccoz, N., Marchand-Maillet, S.: Unsupervised event discrimination based on nonlinear temporal modelling of activity. Pattern Analysis and Application (PAA) 7(4), 402–410 (2004)
Chen, L., Tamer Özsu, M., Oria, V.: Using multi-scale histograms to answer pattern existence and shape match queries. In: SSDBM, pp. 217–226 (2005)
Chomat, O., Crowley, J.L.: Probabilistic recognition of activity using local appearance. In: CVPR, pp. 2104–2109 (1999)
Csurka, G., Dance, C., Fan, L., Williamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision (2004)
Fablet, R., Bouthemy, P., Pérez, P.: Non-parametric motion characterization using causal probabilistic models for video indexing and retrieval. IEEE Trans. on Image Processing 11(4), 393–407 (2002)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistics Quaterly 2, 83–97 (1955)
Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV, pp. 432–439 (2003)
Laptev, I., Lindeberg, T.: Velocity adaptation of space-time interest points. In: ICPR (1), pp. 52–56 (2004)
Lin, C.-Y., Tseng, B.L., Smith, J.R.: Video collaborative annotation forum: Establishing ground-truth labels on large multimedia datasets. In: Proceedings of the TRECVID 2003 Workshop (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Moënne-Loccoz, N., Bruno, E., Marchand-Maillet, S.: Video content representation as salient regions of activity. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 384–392. Springer, Heidelberg (2004)
Moënne-Loccoz, N., Bruno, E., Marchand-Maillet, S.: Interactive partial matching of video sequences in large collections. In: IEEE International Conference on Image Processing, Genova, Italy, 11-14 September (2005)
Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: ICPR (3), pp. 32–36 (2004)
Shi, J., Tomasi, C.: Good features to track. In: CVPR, Seattle (June 1994)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (October 2003)
Smeaton, A.F., Kraaij, W., Over, P.: TRECVID 2003 - An Introduction. In: Proceedings of the TRECVID 2003 Workshop (2003)
Zelnik-Manor, L., Irani, M.: Event-based analysis of video. In: CVPR (2), pp. 123–130 (2001)
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Moënne-Loccoz, N., Bruno, E., Marchand-Maillet, S. (2006). Local Feature Trajectories for Efficient Event-Based Indexing of Video Sequences. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_9
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DOI: https://doi.org/10.1007/11788034_9
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