Summary of Dbhp: Trajectory Imputation in Multi-agent Sports Using Derivative-based Hybrid Prediction, by Hanjun Choi et al.
DBHP: Trajectory Imputation in Multi-Agent Sports Using Derivative-Based Hybrid Prediction
by Hanjun Choi, Hyunsung Kim, Minho Lee, Chang-Jo Kim, Jinsung Yoon, Sang-Ki Ko
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a Derivative-Based Hybrid Prediction (DBHP) framework to accurately impute missing multi-agent trajectory data. The framework combines naive predictions from a Set Transformer neural network with alternative predictions using velocity and acceleration information, weighted to enforce physical constraints on realistic trajectories. This approach improves the accuracy and naturalness of predicted positions, velocities, and accelerations compared to existing methods. The DBHP framework is evaluated on player trajectory imputation in team sports, demonstrating significant performance improvements over baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict where people or things will be in the future based on incomplete data. It’s like trying to guess where someone will be walking next based on where they are now and how fast they’re moving. The new method, called DBHP, does a better job of predicting this than previous methods because it takes into account real-world rules about how people move, like how they can’t suddenly teleport or change direction without warning. This is important for things like tracking players in sports games or predicting the movement of vehicles. |
Keywords
» Artificial intelligence » Neural network » Tracking » Transformer
