Multi-future Trajectory Prediction

9 papers with code • 5 benchmarks • 4 datasets

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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.

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.

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.

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

JunweiLiang/Multiverse 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.

AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction

alessiabertugli/AC-VRNN 17 May 2020

Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.

DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting

alexmonti19/dagnet 26 May 2020

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.

BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation

umautobots/bidireaction-trajectory-prediction 29 Jul 2020

BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy.

Stepwise Goal-Driven Networks for Trajectory Prediction

ChuhuaW/SGNet.pytorch 25 Mar 2021

We propose to predict the future trajectories of observed agents (e. g., pedestrians or vehicles) by estimating and using their goals at multiple time scales.