Pedestrian Trajectory Prediction
37 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Pedestrian Trajectory Prediction
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JAAD
PIE
Euro-PVI
Latest papers
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.
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction
Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning.
Social Interpretable Tree for Pedestrian Trajectory Prediction
lssiair/sit
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Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications.
Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion
Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states.
Non-Probability Sampling Network for Stochastic Human Trajectory Prediction
Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories.
SocialVAE: Human Trajectory Prediction using Timewise Latents
Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions.
Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation
abduallahmohamed/social-implicit
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AMD is a metric that quantifies how close the whole generated samples are to the ground truth.
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
We employ MESRNN for pedestrian trajectory prediction, utilizing these meta-path based features to capture the relationships between the trajectories of pedestrians at different points in time and space.
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction
Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature.
Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction
yutasq/multi-class-social-stgcnn
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This is because they ignore the impact of the implicit correlations between different types of road users on the trajectory to be predicted - for example, a nearby pedestrian has a different level of influence from a nearby car.
