An Approach to Temporal Phase Classification on Videos of the Volleyball's Basic Reception Technique

Jose G. Garcia, Elizabeth R. Villota, C. Beltran Castañon

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Resumen

In this paper we provide an approach on sports analysis using Deep learning techniques. As part of a current project, the volleyball's basic reception technique has been divided into temporal phases. We performed an evaluation over our own labelled dataset consisting in 14814 frames from 69 videos depicting the desired reception technique. A model based on the YOLO algorithm was trained to locate the player region and trim the frames. Two time fusion methods over the frames wereproposed and evaluated with CNN models which were created based on the ResNet models and a transfer learning approach was used to train them. The results show that these models were able of classifying the frames with their corresponding phase with an accuracy of 92.21% in our best model. Also it can be seen that the RGB merging method shown in this paper helps to slightly improve the performance of the models. Furthermore, the models were capable of learning the temporality of the phases as the mistakes done by the models occurred between consecutive phases.

Idioma originalInglés
Título de la publicación alojadaICCDA 2020 - Proceedings of the 4th International Conference on Compute and Data Analysis
EditorialAssociation for Computing Machinery
Páginas148-151
Número de páginas4
ISBN (versión digital)9781450376440
DOI
EstadoPublicada - 9 mar. 2020
Evento4th International Conference on Compute and Data Analysis, ICCDA 2020 - Silicon Valley, San Jose, Estados Unidos
Duración: 9 mar. 202012 mar. 2020

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia4th International Conference on Compute and Data Analysis, ICCDA 2020
País/TerritorioEstados Unidos
CiudadSilicon Valley, San Jose
Período9/03/2012/03/20

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