Trajectory Forecasting
73 papers with code • 4 benchmarks • 16 datasets
Trajectory forecasting is a sequential prediction task, where a forecasting model predicts future trajectories of all moving agents (humans, vehicles, etc.) in a scene, based on their past trajectories and/or the scene context.
(Illustrative figure from Social NCE: Contrastive Learning of Socially-aware Motion Representations)
Benchmarks
These leaderboards are used to track progress in Trajectory Forecasting
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Datasets
UCY
highD Dataset
inD Dataset
JAAD
TrajNet
rounD Dataset
FPL
PIE
exiD Dataset
TrajAir: A General Aviation Trajectory Dataset
Most implemented papers
Real Time Trajectory Prediction Using Deep Conditional Generative Models
sebasutp/trajectory_forcasting
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Our method uses encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations.
Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
olly-styles/Multiple-Object-Forecasting
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In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-level perspective and call for the prediction of full object bounding boxes, rather than trajectories alone.
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
JunweiLiang/Multiverse
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CVPR 2020
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
nachiket92/P2T
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We address the problem of forecasting pedestrian and vehicle trajectories in unknown environments, conditioned on their past motion and scene structure.
Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding
kami93/CMU-DATF
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Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making.
Transformer Networks for Trajectory Forecasting
FGiuliari/Trajectory-Transformer
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In particular, the TF model without bells and whistles yields the best score on the largest and most challenging trajectory forecasting benchmark of TrajNet.
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.
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.
Dynamic Neural Relational Inference
cgraber/cvpr_dNRI
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CVPR 2020
Understanding interactions between entities, e. g., joints of the human body, team sports players, etc., is crucial for tasks like forecasting.
