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Title: Sports video analysis for player strength and weakness psychiatry in the context of object-relational data bases
Authors: Uma, Maheswari
Rajaram, M.
Sivanandam, S.N.
???metadata.dc.contributor.url???: umayam2003@yahoo.co.in
Keywords: Tactical analysis;Sports video mining;Player classification;Mining on object-relational databases;Mining on cricket data;Player strength weakness analysis;Knowledge extraction for tactical training;Sports
Issue Date: 11-Oct-2009
Publisher: Universiti Malaysia Perlis
Citation: p.1B3 1 - 1B3 9
Series/Report no.: Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009)
Abstract: The advent of video coverage of sports has provided an impetus to develop models for tactics analysis for providing training assistance by summarizing the play tactics from video streams. Though there are plenty of sports data and statistics available, there has been no real effort to scientifically extract value from such data. The rapid growth in size of the match database far exceeds the human abilities to analyze such data, thus creating an opportunity for using data mining on this database. The aim of this work is to mine sports video annotation data to extract knowledge about match play sequences and applying that knowledge for classification of players for developing player specific training taxonomy. The major objective of this paper is to analyze individual player’s performances and to devise a classification technique so as to classify them into appropriate groups using the frequently played patterns and other performance indices like strike rate, six-runs and fourruns. This classification helps the coaches to know the current form of the player and to understand their strengths and weaknesses. With this information, a coach can assess the effectiveness of certain coaching decisions and formulate game strategy for subsequent games. To achieve the objective of this work, video stream of cricket matches were observed manually and ball shot descriptions were taken as annotation and stored into an object-relational data model. Frequently occurring patterns were identified, then further evaluation was carried out on those patterns to group them into different clusters based ontheir influence in producing success and failure. Classification mechanism is applied to analyze each and every individual player’s strengths and weaknesses to fix them into a respective class of training taxonomy.
Description: Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
URI: http://hdl.handle.net/123456789/7289
Appears in Collections:Conference Papers

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