Pedestrian Trajectory Prediction
31 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
tensorflow/models
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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.
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.
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras
We refer to our method as SimAug.
Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras
olly-styles/Multi-Camera-Trajectory-Forecasting
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To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras.
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms.
Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians.
