Abstract
In this paper, we present a baseball player behavior analysis system by combining pitch types and swing events. We use eight kinds of semantic scenes detected from baseball videos in our previous work. For the pitch types, we use the characteristic of the ball in a pitch scene to identify the ball trajectory, and then 39 features are extracted to feed into a trained SVM for classifying pitch types. For the swing events, we use moving objects in the batter region to determine whether a swing occurs. Then, the event following the swing is detected using an HMM, based on the after-swing scene sequence. Next, the experimental results show that both pitch type recognition and swing event detection have accuracy rates 91.5% and 91.1%. Finally, we analyze and summarize player behavior by combining pitch types and swing events.
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Huang, YF., Yang, ZX. (2012). Automatic Player Behavior Analyses from Baseball Broadcast Videos. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_2
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DOI: https://doi.org/10.1007/978-3-642-35236-2_2
Publisher Name: Springer, Berlin, Heidelberg
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