102 citations · 129 across the 4 of their papers we have counts for
6 papers
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…
Tracking Pedestrian Heads in Dense Crowd
Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand +1
Tracking humans in crowded video sequences is an important constituent of visual scene understanding. Increasing crowd density challenges visibility of humans, limiting the scalabi…
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…
Properties of pedestrians walking in line: Stepping behavior
Asja Jelić, Cécile Appert-Rolland, Samuel Lemercier +1
In human crowds, interactions among individuals give rise to a variety of self-organized collective motions that help the group to effectively solve the problem of coordination. Ho…