Title :
SME: Learning Automata-Based Algorithm for Estimating the Mobility Model of Soccer Players
Author :
Jamalian, A.H. ; Sefidpour, A.R. ; Manzuri-Shalmani, M.T. ; Iraji, R.
Author_Institution :
Sharif Univ. of Technol., Tehran
Abstract :
Soccer model and relation of players and coach has been analyzed by a learning automata-based method, called soccer mobility estimator (SME), who estimates the mobility model of soccer players. During a soccer match, players play according to a certain program designed by coach. The pattern of players´ mobility is not stochastic and it can be assumed that they are playing with a certain mobility model. Since knowledge about mobility model of nodes in mobile ad-hoc networks has a substantial effect on its performance evaluation, knowledge about mobility model of soccer players can be useful for coaches and experts for game analysis. In fact the mobility model of players could be an important parameter for assessment of team solidarity. Simulation results show that the mobility model of soccer players is similar, up to 66%, to the RPGM (reference point group mobility) mobility model.
Keywords :
automata theory; learning (artificial intelligence); mobile computing; sport; video signal processing; digital video procesing; game analysis; game coaching; learning automata-based algorithm; mobile ad-hoc networks; reference point group mobility; soccer mobility estimator; soccer players; video sequences; Cameras; Computer vision; Data mining; Games; Histograms; Learning automata; Robustness; Solid modeling; Target tracking; Video sequences; Learning Automata; Mobility Model; RPGM; SME; Soccer Analysis;
Conference_Titel :
Cognitive Informatics, 6th IEEE International Conference on
Conference_Location :
Lake Tahoo, CA
Print_ISBN :
9781-4244-1327-0
Electronic_ISBN :
978-1-4244-1328-7
DOI :
10.1109/COGINF.2007.4341925
