Abstract
In this paper, we propose a novel approach for action classification in soccer videos using a recurrent neural network scheme. Thereby, we extract from each video action at each timestep a set of features which describe both the visual content (by the mean of a BoW approach) and the dominant motion (with a key point based approach). A Long Short-Term Memory-based Recurrent Neural Network is then trained to classify each video sequence considering the temporal evolution of the features for each timestep. Experimental results on the MICC-Soccer-Actions-4 database show that the proposed approach outperforms classification methods of related works (with a classification rate of 77 %), and that the combination of the two features (BoW and dominant motion) leads to a classification rate of 92 %.
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References
Ekin, A., Tekalp, A., Mehrotra, R.: Automatic Soccer Video Analysis and Summarization. IEEE Transactions on Image Processing 12(7) (2003)
Gong, Y., Lim, T., Chua, H.: Automatic Parsing of TV Soccer Programs. In: IEEE International Conference on Multimedia Computing and Systems, pp. 167–174 (1995)
Ballan, L., Bertini, M., Del Bimbo, A., Serra, G.: Action categorization in soccer videos using string kernels. In: Proc. of IEEE CBMI, Chania, Crete (2009)
Gers, F., Schraudolph, N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. The Journal of Machine Learning Research 3, 115–143 (2003)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International journal of computer vision 60(2), 91–110 (2004)
Wolf, C., Jolion, J., Chassaing, F.: Text Localization, Enhancement and Binarization in Multimedia Documents. In: Proc. of ICPR (2002)
Fischler, M.: RANSAC: A Paradigm for Model Fitting With Applications to Image Analysis and Automated Cartography. Communications of the ACM (1981)
Delakis, E.: Multimodal Tennis Video Structure Analysis with Segment Models. PhD thesis, Université de Rennes 1 (2006)
Bishop, C.: Neural networks for pattern recognition. Oxford Univ. Press, Inc., Oxford (2005)
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Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A. (2010). Action Classification in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_20
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DOI: https://doi.org/10.1007/978-3-642-15822-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
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