Motion-based event detection and semantic classification for baseball sport videos

The techniques of event detection and semantic classification of baseball sport videos are investigated. Due to abundant motion information in sport videos, motion vectors are estimated, validated, and used to compute both the motion activity and camera motion parameters of a frame. Considering the domain-specific knowledge of baseball sport, behaviors of motion features in the spatial or temporal domain are analyzed for segmenting the whole baseball video into a lot of play events (defined as the time interval between two pitching shots), from which a key shot is identified for semantic classification. Taking motion features of key shots as the input of a neural network, our proposed system is capable of classifying segmented events into three semantic categories as "non-hitting", "in-field", and "out-field". According to experiments on more than 200 events, we can achieve a classification rate of 91.55%. The proposed technique will be helpful in applications of baseball video summary and retrieval.