25 citations · 27 across the 5 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…
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…
Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift
Edgar Sucar, Jean-Bernard Hayet
This work proposes a new, online algorithm for estimating the local scale correction to apply to the output of a monocular SLAM system and obtain an as faithful as possible metric…
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
Edgar Sucar, Jean-Bernard Hayet
This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integra…