Pedestrian Trajectory Prediction
46 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Pedestrian Trajectory Prediction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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JAAD
PIE
Euro-PVI
Most implemented papers
Deep Residual Learning for Image Recognition
KaimingHe/resnet-1k-layers
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
shuaishiliu/SGCN
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Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction.
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction
A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations.
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Khrylx/AgentFormer
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ICCV 2021
Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time.
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
mediabrain-sjtu/eqmotion
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CVPR 2023
In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle.
SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction
cocoon2wong/socialcircleplus
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Trajectory prediction is a crucial aspect of understanding human behaviors.
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction
Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians but ignore the scene layouts in pedestrian trajectory prediction.
Encoding Crowd Interaction With Deep Neural Network for Pedestrian Trajectory Prediction
Specifically, motivated by the residual learning in deep learning, we propose to predict displacement between neighboring frames for each pedestrian sequentially.
SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
zhangpur/SR-LSTM
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CVPR 2019
In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism.
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
amiryanj/socialways
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CVPR 2019
We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution.
