Intelligent Middleware for Adaptive Sensing of Tennis Coaching Sessions

In professional tennis training matches, the coachneeds to be able to view play from the most appropriate anglein order to monitor players activities. In this paper, we presenta system which can adapt the operation of a series of camerasin order to maintain optimal system performance based on aset of wireless sensors. This setup is used as a testbed for anagent based intelligent middleware that can correlate data frommany different wired and wireless sensors and provide effectivein-situ decision making. The proposed solution is flexible enoughto allow the addition of new sensors and actuators. Within thissetup we also provide details of a case study for the embeddedcontrol of cameras through the use of Ubisense data.

[1]  Gregory M. P. O'Hare,et al.  Beyond Prototyping in the Factory of Agents , 2003, CEEMAS.

[2]  Jürgen Dix,et al.  Multi-Agent Programming , 2009, Springer US.

[3]  Huang Lee,et al.  Sub-optimal Camera Selection in Practical Vision Networks through Shape Approximation , 2008, ACIVS.

[4]  E. Paul Roetert,et al.  Fitness Comparisons Among Three Different Levels of Elite Tennis Players , 1996 .

[5]  Greg M. P. O'Hare Agent factory: an environment for the fabrication of multiagent systems , 1996 .

[6]  Gregory M. P. O'Hare,et al.  Agent Factory Micro Edition: A Framework for Ambient Applications , 2006, International Conference on Computational Science.

[7]  Hisashi Miyamori,et al.  Video annotation for content-based retrieval using human behavior analysis and domain knowledge , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[8]  Alan F. Smeaton,et al.  TennisSense: A Multi-Modal Sensing Platform for Sport , 2009, ERCIM News.

[9]  Yoav Shoham,et al.  Agent-Oriented Programming , 1992, Artif. Intell..

[10]  Alan F. Smeaton,et al.  A Sensing Platform for Physiological and Contextual Feedback to Tennis Athletes , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[11]  Noel E. O'Connor,et al.  Event detection in field sports video using audio-visual features and a support vector Machine , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Yves Jean,et al.  Ball tracking and virtual replays for innovative tennis broadcasts , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  William J. Christmas,et al.  A Tennis Ball Tracking Algorithm for Automatic Annotation of Tennis Match , 2005, BMVC.

[14]  Gregory M. P. O'Hare,et al.  Towards Pervasive Intelligence: Reflections on the Evolution of the Agent Factory Framework , 2009, Multi-Agent Programming, Languages, Tools and Applications.

[15]  Rafael H. Bordini,et al.  Multi-Agent Programming: Languages, Platforms and Applications , 2005, Multi-Agent Programming.

[16]  Anand S. Rao,et al.  BDI Agents: From Theory to Practice , 1995, ICMAS.

[17]  M. Kovacs,et al.  Applied physiology of tennis performance , 2006, British Journal of Sports Medicine.