Motion Forecasting

58 papers with code • 1 benchmarks • 12 datasets

Motion forecasting is the task of predicting the location of a tracked object in the future

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.

Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

StanfordASL/Trajectron-plus-plus ECCV 2020

Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.

TNT: Target-driveN Trajectory Prediction

henry1iu/tnt-trajectory-prediction 19 Aug 2020

Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.

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.

Argoverse: 3D Tracking and Forecasting with Rich Maps

argoai/argoverse-api CVPR 2019

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.

One Thousand and One Hours: Self-driving Motion Prediction Dataset

wenkaip-1836890/pyromid_l5prediction 25 Jun 2020

Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

DerrickXuNu/OpenCOOD ECCV 2020

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.

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.

Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

bchimagine/DeepPredictiveMotionTracking 25 Sep 2019

Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient.

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.