Spatio-Temporal Player Relation Modeling for Tactic Recognition in Sports Videos

Abstract

Tactic recognition in sports videos is a challenging task. To address this, we present a novel spatio-temporal relation modeling approach, which captures both detailed player interactions and long-range group dynamics in tactics. In spatial modeling, we propose an Adaptive Graph Convolutional Network (A-GCN), and it represents individual and common patterns of data through local and global graphs to learn diverse player interactions. In temporal modeling, we propose an Attentive Temporal Convolutional Network (A-TCN) and with spatial configurations as input, it builds group dynamics and is robust to redundant content by considering sequence dependencies. Due to adaptive interaction and attentive dynamics modeling, our approach is able to comprehensively describe team cooperation over time in a tactic. We extensively evaluate the proposed approach on the Volleyball dataset and a newly collected VolleyTactic dataset, and the experimental results show its advantage.