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The application of visual tracking to the performance analysis of sportsplayers in dynamic competitions is vital for effective coaching. In doublesmatches, coordinated positioning is crucial for maintaining control of thecourt and minimizing opponents' scoring opportunities. The analysis of suchteamwork plays a vital role in understanding the dynamics of the game. However,previous studies have primarily focused on analyzing and assessing singlesplayers without considering occlusion in broadcast videos. These studies haverelied on discrete representations, which involve the analysis andrepresentation of specific actions (e.g., strokes) or events that occur duringthe game while overlooking the meaningful spatial distribution. In this work,we present the first annotated drone dataset from top and back views inbadminton doubles and propose a framework to estimate the control areaprobability map, which can be used to evaluate teamwork performance. We presentan efficient framework of deep neural networks that enables the calculation offull probability surfaces. This framework utilizes the embedding of a Gaussianmixture map of players' positions and employs graph convolution on their poses.In the experiment, we verify our approach by comparing various baselines anddiscovering the correlations between the score and control area. Additionally,we propose a practical application for assessing optimal positioning to provideinstructions during a game. Our approach offers both visual and quantitativeevaluations of players' movements, thereby providing valuable insights intodoubles teamwork. The dataset and related project code is available athttps://github.com/Ning-D/Drone_BD_ControlArea

Estimation of control area in badminton doubles with pose information from top and back view drone videos