Abstract
This paper proposes a local motion-based approach for recognizing group activities in soccer videos. Given the SIFT keypoint matches on two successive frames, we propose a simple but effective method to group these keypoints into the background point set and the foreground point set. The former one is used to estimate camera motion and the latter one is applied to represent group actions. After camera motion compensation, we apply a local motion descriptor to characterize relative motion between corresponding keypoints on two consecutive frames. The novel descriptor is effective in representing group activities since it focuses on local motion of individuals and excludes noise such as background motion caused by inaccurate compensation. Experimental results show that our approach achieves high recognition rates in soccer videos and is robust to inaccurate compensation results.
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References
Shi, J., Tomasi, C.: Good Features to Track. In: Proc. CVPR (1994)
Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University (April 1991)
Jung, B., Sukhatme, G.S.: Real-time motion tracking from a mobile robot. Technical report, University of Southern California (2005)
Kong, Y., Zhang, X., Wei, Q., Hu, W., Jia, Y.: Group action recognition in soccer videos. In: Proc. ICPR (2008)
Laptev, I., Lindeberg, T.: Space-time interest points. In: IEEE ICCV (2003)
Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proc. ACM Multimedia (2007)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Zhang, Z., Hu, Y., Chan, S., Chia, L.T.: Motion context: A new representation for human action recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 817–829. Springer, Heidelberg (2008)
Liu, J., Shah, M.: Learning Human Actions via Information Maximization. In: Proc. CVPR (2008)
Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. In: BMVC (2006)
Niebles, J.C., Fei-Fei, L.: A hierarchical model of shape and appearance for human action classification. In: IEEE CVPR (2007)
Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79(3), 299–318 (2008)
Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: IEEE ICPR (2004)
Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: Proc. 9th Int. Conf. Computer Vision, vol. 2, pp. 726–733 (2003)
Zhu, G., Xu, C., Huang, Q., Gao, W.: Action Recognition in Broadcast Tennis Video. In: Proc. ICPR (2006)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop, pp. 121–130 (1981)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Kong, Y., Hu, W., Zhang, X., Wang, H., Jia, Y. (2010). Learning Group Activity in Soccer Videos from Local Motion. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_10
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DOI: https://doi.org/10.1007/978-3-642-12307-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12306-1
Online ISBN: 978-3-642-12307-8
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