Pedestrian Trajectory Prediction

37 papers with code • 1 benchmarks • 3 datasets

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Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models 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 4 Apr 2021

Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction.

AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting

Khrylx/AgentFormer 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.

Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction

inhwanbae/gpgraph 20 Jul 2022

A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations.

SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction

xuehaouwa/SS-LSTM IEEE Winter Conference on Applications of Computer Vision 2018

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

svip-lab/CIDNN CVPR 2018

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 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 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.

Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras

olly-styles/Multi-Camera-Trajectory-Forecasting 1 May 2020

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.