• Corpus ID: 229340538

A Graph Attention Based Approach for Trajectory Prediction in Multi-agent Sports Games

@article{Ding2020AGA,
  title={A Graph Attention Based Approach for Trajectory Prediction in Multi-agent Sports Games},
  author={Ding Ding and Huimin Huang},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.10531}
}
This work investigates the problem of multi-agents trajectory prediction. Prior approaches lack of capability of capturing fine-grained dependencies among coordinated agents. In this paper, we propose a spatial-temporal trajectory prediction approach that is able to learn the strategy of a team with multiple coordinated agents. In particular, we use graph-based attention model to learn the dependency of the agents. In addition, instead of utilizing the recurrent networks (e.g., VRNN, LSTM), our… 
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Graph Neural Networks to Predict Sports Outcomes

A sport-agnostic, graph-based representation of game states is introduced that uses the proposed graph representation as input to graph neural networks to predict sports outcomes and preserves permutation invariance and allows for flexible player interaction weights.

References

SHOWING 1-10 OF 21 REFERENCES

Diverse Generation for Multi-Agent Sports Games

A new generative model for multi-agent trajectory data, focusing on the case of multi-player sports games, that leverages graph neural networks and variational recurrent neural networks to achieve a permutation equivariant model suitable for sports.

Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories

The results show that the proposed tree-based method uses a hierarchical structure to perform multi-agent data alignment and partitioning in a coarse-to-fine fashion boosts performance in retrieving similar sports plays -- especially in interactive situations where the user selects a subset of trajectories compared to current state-of-the-art methods.

Generating Long-term Trajectories Using Deep Hierarchical Networks

This work proposes a hierarchical policy class that automatically reasons about both long-term and short-term goals, which is instantiate as a hierarchical neural network and generates significantly more realistic trajectories compared to non-hierarchical baselines as judged by professional sports analysts.

Stochastic Prediction of Multi-Agent Interactions from Partial Observations

We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.…

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

This paper proposes a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain, and builds the model with complete convolutional structures, which enable much faster training speed with fewer parameters.

Where Will They Go? Predicting Fine-Grained Adversarial Multi-agent Motion Using Conditional Variational Autoencoders

This paper presents a technique using conditional variational autoencoder which learns a model that “personalizes” prediction to individual agent behavior within a group representation and shows that this approach efficiently predicts context-specific agent motions.

Representing and Discovering Adversarial Team Behaviors Using Player Roles

This paper describes a method to represent and discover adversarial group behavior in a continuous domain and shows that its compact representation can effectively "denoise" erroneous detections as well as enabling temporal analysis, which was previously prohibitive due to the dimensionality of the signal.

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

A novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Experiments on two real-world datasets from the Caltrans Performance Measurement System demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.

Coordinated Multi-Agent Imitation Learning

It is shown that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines, and the method integrates unsupervised structure learning with conventional imitation learning.