Abstract
This paper introduces a real-time action recognition and tactical-behavior mining system designed specifically for volleyball games. The system aims to provide data augmentation, video annotation and KPI extraction processes by accurately identifying various actions and action sequential patterns performed during volleyball matches. Leveraging advanced computer vision techniques, the system aims at automatically detecting and recognizing player actions and group actions in real time. Then, Process Mining techniques are used to extract tactical behaviors, in the form of temporal relations, among player actions. By providing precise annotations, the system significantly provides an instrument for volleyball game analytics and tactical analysis. This paper outlines the architecture and key components of the real-time action recognition and tactical-behavior mining system and presents some preliminary results on the performance of the proposed model.