Computer vision is steadily gaining importance in many research fields, as its applications expand from traditional fields situation analysis and scene understanding in video surveillance to other scenarios. The sportive context can represent a perfect test-bed for many machine vision algorithms because of the large availability of visual data brought by wide spread cameras on a relatively high number of courts. In this paper we introduce a tennis ball detection and tracking method that exploits domain knowledge to effectively recognize ball positions and trajectories. A peculiarity of this approach is that it starts from a sparse but cluttered point cloud that evolves over time, basically working on 3D samples only. Experiments on real data demonstrate the effectiveness of the algorithm in terms of tracking accuracy and path following capability.

Real-time tracking of a tennis ball by combining 3D data and domain knowledge / Reno', Vito; Nicola, Mosca; Massimiliano, Nitti; Guaragnella, Cataldo; Tiziana, D'Orazio; Ettore, Stella. - (2016). (Intervento presentato al convegno International Conference on Technology and Innovation in Sports, Health and Wellbeing, TISHW 2016 tenutosi a Vila Real, Portugal nel December 1-3, 2016) [10.1109/TISHW.2016.7847774].

Real-time tracking of a tennis ball by combining 3D data and domain knowledge

RENO', VITO;GUARAGNELLA, Cataldo;
2016-01-01

Abstract

Computer vision is steadily gaining importance in many research fields, as its applications expand from traditional fields situation analysis and scene understanding in video surveillance to other scenarios. The sportive context can represent a perfect test-bed for many machine vision algorithms because of the large availability of visual data brought by wide spread cameras on a relatively high number of courts. In this paper we introduce a tennis ball detection and tracking method that exploits domain knowledge to effectively recognize ball positions and trajectories. A peculiarity of this approach is that it starts from a sparse but cluttered point cloud that evolves over time, basically working on 3D samples only. Experiments on real data demonstrate the effectiveness of the algorithm in terms of tracking accuracy and path following capability.
2016
International Conference on Technology and Innovation in Sports, Health and Wellbeing, TISHW 2016
978-1-5090-5727-6
Real-time tracking of a tennis ball by combining 3D data and domain knowledge / Reno', Vito; Nicola, Mosca; Massimiliano, Nitti; Guaragnella, Cataldo; Tiziana, D'Orazio; Ettore, Stella. - (2016). (Intervento presentato al convegno International Conference on Technology and Innovation in Sports, Health and Wellbeing, TISHW 2016 tenutosi a Vila Real, Portugal nel December 1-3, 2016) [10.1109/TISHW.2016.7847774].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/95101
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