Using Artificial Vision Techniques for Individual Player Tracking in Sport Events

• Roberto López Castro ^[1] ; Diego Andrade Canosa ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: XoveTIC 2019: The 2nd XoveTIC Conference (XoveTIC 2019), A Coruña, Spain, 5–6 September / Alberto Alvarellos González (ed. lit.), Joaquim de Moura (ed. lit.), Beatriz Botana Barreiro (ed. lit.), Javier Pereira-Loureiro (ed. lit.), Manuel Francisco González Penedo (ed. lit.), 2019, ISBN 978-3-03921-444-0
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • We introduce a hybrid approach that can track an individual football player in a video sequence. This solution achieves a good balance between speed and accuracy, combining traditional object tracking techniques with Deep Neural Networks (DNN). While traditional techniques lack accuracy, the main shortcoming of DNN is performance. Both types of techniques complement to each other to provide an accurate and fast object tracking approach that does not require human intervention. The accuracy of our solution has been validated using the SoccerNet Dataset against hand annotated video sequences. For the tracking of 4 different players of 2 different teams our approach has achieved an Area Under Curve (AUC) of 0.66, in terms of accuracy, and a frame rate of 91.75 FPS, in terms of performance, running on a Nvidia GTX 1080Ti GPU.