Statecharts: A visual formalism for complex systems
Science of Computer Programming
Applied Artificial Intelligence
From image sequences towards conceptual descriptions
Image and Vision Computing
A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Tracking of Soccer Players Based on Data Fusion
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
Analysis of Player Actions in Selected Hockey Game Situations
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Ice hockey shot event modeling with mixture hidden Markov model
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Ice hockey shooting event modeling with mixture hidden Markov model
Multimedia Tools and Applications
Take your eyes off the ball: Improving ball-tracking by focusing on team play
Computer Vision and Image Understanding
Hi-index | 0.00 |
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 player's 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.