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

Abstract

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 t