A technology platform for automatic high-level tennis game analysis

• Published in: Computer vision and image understanding 2017-06, Vol.159, p.164-175
• Main Authors: Renò, Vito, Mosca, Nicola, Nitti, Massimiliano, D’Orazio, Tiziana, Guaragnella, Cataldo, Campagnoli, Donato, Prati, Andrea, Stella, Ettore
• Format: Article
• Language: English
• Subjects: Ball and player tracking; Semantic interaction interpretation; Synchronized cameras platform; Trajectory analysis
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2017.01.002

•Automatic system for actions annotation in tennis video sequences for coaching needs.•3D ball trajectory reconstruction and player position detection.•Strokes, serves, and bounces recognition from ball trajectory changes.•Actions score evaluation using a Finite State Machine. Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours.