Abstract
This paper presents a set of novel features for classifying basketball video clips into semantic events and a simple way to use prior temporal context information to improve the accuracy of classification. Specifically, the feature set consists of a motion descriptor, motion histogram, entropy of the histogram and texture. The motion descriptor is defined based on a set of primitive motion patterns which are derived form optical flow field. The event recognition is achieved by using kernel SVMs and a temporal contextual model. Experimental results have verified the effectiveness of the proposed method.
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Li, L., Chen, Y., Hu, W., Li, W., Zhang, X. (2009). Recognition of Semantic Basketball Events Based on Optical Flow Patterns. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_45
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DOI: https://doi.org/10.1007/978-3-642-10520-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10519-7
Online ISBN: 978-3-642-10520-3
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