MIDAS: Multi-Agent Trajectory Imputation in Sports

MIDAS: Multi-Agent Trajectory Imputation in SportsThe episode collectively addresses the critical problem of missing data in multi-agent sports trajectory analysis, a common challenge due to factors like camera occlusions and data confidentiality. "Trajectory Imputation in Multi-Agent Sports with Derivative-Accumulating Self-Ensemble" introduces MIDAS, a novel framework that accurately infers missing player movements by predicting positions, velocities, and accelerations, even with limited training data. This approach is contrasted with traditional interpolation techniques and machine learning regression algorithms explored in "FOOTBALL ANALYTICS BASED ON PLAYER TRACKING DATA USING INTERPOLATION TECHNIQUES FOR THE PREDICTION OF MISSING COORDINATES," which also aims to reconstruct incomplete sports data. "Missing Data in Time Series" provides a broader overview of time series imputation methods, categorizing them and discussing their suitability based on data characteristics. Finally, "TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports" presents a unified transformer-based model that not only handles trajectory imputation and forecasting but also classifies global states within multi-agent sports scenarios, highlighting the use of attention mechanisms for capturing complex interactions.

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