Trajectory Forecasting
45 papers with code • 4 benchmarks • 9 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Datasets
UCY
JAAD
TrajNet
PIE
FPL
NBA SportVU
TrajAir: A General Aviation Trajectory Dataset
Euro-PVI
Most implemented papers
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
agrimgupta92/sgan
•
•
CVPR 2018
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Social NCE: Contrastive Learning of Socially-aware Motion Representations
vita-epfl/social-nce
•
•
ICCV 2021
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
vincent-leguen/STDL
•
•
NeurIPS 2019
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
HarshayuGirase/PECNet
•
•
ECCV 2020
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
google/next-prediction
•
•
CVPR 2019
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
abduallahmohamed/Social-STGCNN
•
•
CVPR 2020
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans.
Exploring Dynamic Context for Multi-path Trajectory Prediction
wtliao/DCENet
•
•
In our framework, first, the spatial context between agents is explored by using self-attention architectures.
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
harshayugirase/human-path-prediction
•
•
ICCV 2021
Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions.
Real-Time Forecasting of Driver-Vehicle Dynamics on 3D Roads: a Deep-Learning Framework Leveraging Bayesian Optimisation
This represents a particularly useful problem, for instance in autonomous driving, but it does not cover a spectrum of applications in control and simulation that require information on vehicle dynamics features other than pose and orientation.
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
coolsunxu/sophie
•
•
CVPR 2019
Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors.
