A survey of computer vision methods for 2d object detection from unmanned aerial vehicles
The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many
applications fields. Most investigated research topics focus on increasing autonomy during …
applications fields. Most investigated research topics focus on increasing autonomy during …
Stcrowd: A multimodal dataset for pedestrian perception in crowded scenes
Accurately detecting and tracking pedestrians in 3D space is challenging due to large
variations in rotations, poses and scales. The situation becomes even worse for dense …
variations in rotations, poses and scales. The situation becomes even worse for dense …
From perception to navigation in environments with persons: An indoor evaluation of the state of the art
Research in the field of social robotics is allowing service robots to operate in environments
with people. In the aim of realizing the vision of humans and robots coexisting in the same …
with people. In the aim of realizing the vision of humans and robots coexisting in the same …
Frozone: Freezing-free, pedestrian-friendly navigation in human crowds
We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that
arises when a robot navigates through dense scenarios and crowds. Our method senses …
arises when a robot navigates through dense scenarios and crowds. Our method senses …
Autonomous social distancing in urban environments using a quadruped robot
Corona Virus Disease 2019 (COVID-19) pandemic has become a global challenge faced by
people all over the world. Social distancing has been proved to be an effective practice to …
people all over the world. Social distancing has been proved to be an effective practice to …
Cmetric: A driving behavior measure using centrality functions
We present a new measure, CMetric, to classify driver behaviors using centrality functions.
Our formulation combines concepts from computational graph theory and social traffic …
Our formulation combines concepts from computational graph theory and social traffic …
Using graph-theoretic machine learning to predict human driver behavior
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic
environment composed of human drivers and do not adapt to local conditions and socio …
environment composed of human drivers and do not adapt to local conditions and socio …
Stylepredict: Machine theory of mind for human driver behavior from trajectories
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic
environment composed of human drivers and do not adapt to local conditions and socio …
environment composed of human drivers and do not adapt to local conditions and socio …
Graphrqi: Classifying driver behaviors using graph spectrums
We present a novel algorithm (GraphRQI) to identify driver behaviors from road-agent
trajectories. Our approach assumes that the road-agents exhibit a range of driving traits …
trajectories. Our approach assumes that the road-agents exhibit a range of driving traits …
Embedding group and obstacle information in lstm networks for human trajectory prediction in crowded scenes
Recurrent neural networks have shown good abilities in learning the spatio-temporal
dependencies of moving agents in crowded scenes. Recently, they have been adopted to …
dependencies of moving agents in crowded scenes. Recently, they have been adopted to …