Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM
arXiv:2306.08206 · doi:10.1145/3580305.3599779
Abstract
As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics.
References in corpus (11)
- InceptionTime: Finding AlexNet for Time Series Classification
- A Recurrent Latent Variable Model for Sequential Data
- BRITS: Bidirectional Recurrent Imputation for Time Series
- Actions Speak Louder Than Goals: Valuing Player Actions in Soccer
- PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach
- Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
- Generating Multi-Agent Trajectories using Programmatic Weak Supervision
- NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
- Player Chemistry: Striving for a Perfectly Balanced Soccer Team
- Automatic event detection in football using tracking data
- Smart Director: An Event-Driven Directing System for Live Broadcasting