25 citations · 27 across the 5 of their papers we have counts for
6 papers
Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review
Céline Finet, Stephane Da Silva Martins, Jean-Bernard Hayet +5
With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand's reach, with im…
AggNet: Learning to Aggregate Faces for Group Membership Verification
Marzieh Gheisari, Javad Amirian, Teddy Furon +1
In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity. Some existing methods, propose a…
What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing
Javad Amirian, Jean-Bernard Hayet, Julien Pettre
We consider the navigation of mobile robots in crowded environments, for which onboard sensing of the crowd is typically limited by occlusions. We address the problem of inferring…
OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
Javad Amirian, Bingqing Zhang, Francisco Valente Castro +3
Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing…
Data-Driven Crowd Simulation with Generative Adversarial Networks
Javad Amirian, Wouter van Toll, Jean-Bernard Hayet +1
This paper presents a novel data-driven crowd simulation method that can mimic the observed traffic of pedestrians in a given environment. Given a set of observed trajectories, we…
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
Javad Amirian, Jean-Bernard Hayet, Julien Pettre
This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible prediction…