Temporal feature induction for baseball highlight classification
Proceedings of the 15th international conference on Multimedia
Rule-based Event Detection of Broadcast Baseball Videos Using Mid-level Cues
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Event Detection of Broadcast Baseball Videos
IEEE Transactions on Circuits and Systems for Video Technology
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Despite a lot of research efforts in baseball video processing in recent years, little work has been done in analyzing the detailed semantic baseball event detection. This paper presents an effective and efficient baseball event classification system for broadcast baseball videos. Utilizing the specifications of the baseball field and the regularity of shot transition, the system recognizes highlight in video clips and identifies what semantic baseball event of the baseball clips is currently proceeding. First, a video is segmented into several highlights starting with a PC (Pitcher and Catcher) shot and ending up with some specific shots. Before every baseball event classifier is designed, several novel schemes including some specific features such as soil percentage and objects extraction such as first base are applied. The extracted mid-level cues are used to develop baseball event classifiers based on an HMM (Hidden Markov model). Due to specific features detection, more hitting baseball events are detected and the simulation results show that the classification of twelve significant baseball events is very promising.