Event detection in baseball video using superimposed caption recognition
Proceedings of the tenth ACM international conference on Multimedia
Scene-based event detection for baseball videos
Journal of Visual Communication and Image Representation
Automatic Pitch Type Recognition from Baseball Broadcast Videos
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Semantic scene detection system for baseball videos based on the MPEG-7 specification
Proceedings of the 2010 ACM Symposium on Applied Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An event-based video retrieval system by combining broadcasting baseball video and web-casting text
Proceedings of the 2011 ACM Symposium on Applied Computing
Semantic event detection in baseball videos based on a multi-output hidden Markov model
Proceedings of the 2011 ACM Symposium on Applied Computing
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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.