Rule-based Event Detection of Broadcast Baseball Videos Using Mid-level Cues

This paper presents an effective and efficient event detection system for broadcast baseball videos. It integrates mid-level cues including scoreboard information and shot transition patterns into event classification rules. First, a simple scoreboard detection and recognition scheme is developed to extract the game status from videos. Then, a shot transition classifier is designed to obtain the shot transition patterns. The extracted mid-level cues are used to develop an event classifier based on a Bayesian belief network. Using the inference results of the network, we further derive a set of classification rules to identify baseball events. The set of rules is stored in a look-up table such that the classification is only a simple table look-up operation. The simulation results indicate that it identifies ten significant baseball events with 95% of precision rate and 89% of recall rate, which is very promising.