Trajectory Forecasting
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Argoverse: 3D Tracking and Forecasting with Rich Maps
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
AUTONOMOUS VEHICLES MOTION FORECASTING OBJECT TRACKING TRAJECTORY FORECASTING
Argoverse: 3D Tracking and Forecasting With Rich Maps
We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark.
3D MULTI-OBJECT TRACKING AUTONOMOUS VEHICLES MOTION FORECASTING OBJECT TRACKING TRAJECTORY FORECASTING
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
CVPR 2019
• google/next-prediction
• 
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Ranked #1 on
Activity Prediction
on ActEV
ACTIVITY PREDICTION FUTURE PREDICTION HUMAN MOTION PREDICTION MOTION FORECASTING MULTI-TASK LEARNING TRAJECTORY FORECASTING
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
NeurIPS 2019
• vincent-leguen/DILATE
• 
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
NeurIPS 2019
• vincent-leguen/STDL
•
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
CVPR 2019
• amiryanj/socialways
•
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.
HUMAN MOTION PREDICTION MULTI-FUTURE TRAJECTORY PREDICTION SELF-DRIVING CARS TRAJECTORY FORECASTING
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
CVPR 2020
• JunweiLiang/Multiverse
•
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.
Ranked #1 on
Trajectory Prediction
on ForkingPaths
AUTONOMOUS DRIVING HUMAN MOTION PREDICTION MULTI-FUTURE TRAJECTORY PREDICTION SELF-DRIVING CARS TRAJECTORY FORECASTING
Transformer Networks for Trajectory Forecasting
• FGiuliari/Trajectory-Transformer
•
In particular, the TF model without bells and whistles yields the best score on the largest and most challenging trajectory forecasting benchmark of TrajNet.
Ranked #1 on
Trajectory Prediction
on ETH/UCY
Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
• olly-styles/Multiple-Object-Forecasting
•
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.
Ranked #1 on
Multiple Object Forecasting
on Citywalks
Joint 3D Tracking and Forecasting with Graph Neural Network and Diversity Sampling
• xinshuoweng/GNNTrkForecast
•
To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units.