Temporal Segmentation of Group Motion using Gaussian Mixture Models
Computer Vision Winter Workshop, 2008
This paper presents a new trajectory-based approach for probabilistic temporal segmentation of team sports. The probabilistic game model is applied to the player-trajectory data in order to segment individual game instants into one of the three game phases (offensive game, defensive game and time-outs) and a nonlinear or Gaussian smoothing kernel is used to enforce the temporal continuity of the game. The presented approach is compared to the Support Vector Machine (SVM) classifier on three basketball and three handball matches. The obtained results suggest that our approach is general and robust and as such could be applied to various team sports. It can handle unusual game situations such as player exclusions, substitution or injuries which may happen during the game.