Video analysis of hockey play in selected game situations
We present a proof of concept system to represent and reason about hockey play. The system takes as input player motion trajectory data tracked from game video and supported by knowledge of hockey strategy, game situation and specific player profiles. The raw motion trajectory data consists of space-time point sequences of player position registered to rink coordinates. The raw data is augmented with knowledge of forward/backward skating, possession of the puck and specific player attributes (e.g., shoots left, shoots right). We use a Finite State Machine (FSM) model to represent our total knowledge of each given situation. Most state transitions correspond to specific player actions (e.g., pass, shoot). Each transition has an associated Event Evaluation Function (EEF) to assign an immediate reward to the associated action. EEFs can take into account each players spatio-temporal trajectory. Based on the augmented trajectory data, the FSMs and the EEFs, we describe what happened in each identified situation, assess the outcome, estimate when and where key play choices were made, and attempt to predict whether better alternatives were available to achieve understood goals. A textual natural language description and a simple 2D graphic animation of the analysis are produced as the output. The design is flexible to allow the substitution of different analysis modules and extensible to allow the inclusion of additional hockey situations. This paper extends the one published in CRV2005.
finite state machine visual tracking motion analysis