T-GAN segments football matches into intention-driven in-possession phases using player interaction graphs and transformers on 25 Hz tracking data from seven Bundesliga matches, achieving frame-level F1 of 0.87 for intentions.
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A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
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Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
T-GAN segments football matches into intention-driven in-possession phases using player interaction graphs and transformers on 25 Hz tracking data from seven Bundesliga matches, achieving frame-level F1 of 0.87 for intentions.
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Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.