Tracking Players in Indoor Sports Using a Vision System Inspired in Fuzzy and Parallel Processing

Sports are an important part of nowadays society and there is an increasing interest by the sports’ community on having mechanisms that allow them to better understand the dynamics of teams (their own and their opponents). This information is frequently extracted manually by operators that, after the game, visualize game recordings (frequently TV footages) and perform hand annotation, which is a time consuming and error prone task. There is a clear necessity for developing automatic mechanisms and methodologies which allow performing these tasks much faster and systematically. The importance of such systems was first highlighted in the late 80’s by Franks et al. ( (Franks & Nagelkerke, 1988; Franks et al., 1987) ). In this chapter, we present an automatic and intelligent visual system for detecting and tracking handball players based on two cameras that cover the entire playing area. The followed methodology includes the identification of foreground pixels using dynamic background subtraction, the definition of colour subspaces for each team using a Fuzzy inspired model that allows detecting the players based on the colour properties of their clothes. Player tracking is further improved by using one Kalman Filter per player (object to track). The resulting information is aggregated in an undistorted image view of the entire field that is very interesting and meaningful to the target end-user. The generation of the video is a demanding computational task that takes advantage of using parallel computing. The resulting videos are interesting and include several important informations to the human end-user. Tests were conducted on videos collected during the Handball Portuguese SuperCup competition, where the best six Portuguese teams competed for the trophy of the year of 2010/11. The chapter structure is as follow: the next section presents some relevant information on image segmentation methodologies, parallel processing and implementations of automatic visual systems for detecting and tracking players and describes the main methodologies used. Section 3 discusses the proposed architecture principles, providing an overview of the methodology used. Section 4 presents the results achieved and finally section 5 concludes this chapter with the main conclusions. 6