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Vision-based Drone Flocking in Outdoor Environments
Related publications (43)
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We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edg ...
2022Tracking and Relative Localization of Drone Swarms With a Vision-Based Headset
Dario Floreano, Fabrizio Schiano, Maxim Pavliv, Giuseppe Loianno
We address the detection, tracking, and relative localization of the agents of a drone swarm from a human perspective using a headset equipped with a single camera and an Inertial Measurement Unit (IMU). We train and deploy a deep neural network detector o ...
2021Perceiving Humans: from Monocular 3D Localization to Social Distancing
Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni
Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...
2021On the benefits of robust models in modulation recognition
Pascal Frossard, Javier Alejandro Maroto Morales
Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. However, in other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations, which consist of impercep ...
2021From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search
Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...
EPFL2021Pedestrian Intention Prediction: A Convolutional Bottom-Up Multi-Task Approach
Alexandre Massoud Alahi, Taylor Ferdinand Mordan
The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...
2021A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras
Auke Ijspeert, Alessandro Crespi, Mehmet Hasan Mutlu, Simon Lukas Hauser, Jorg Conradt, Ibrahim Youssef Youssef, Alexandre Bernardino
The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a chall ...
2020Deep Generative Models and Applications
Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...
EPFL2020Optimization for Reinforcement Learning: From a single agent to cooperative agents
Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, s ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020Learning stereo reconstruction with deep neural networks
Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed.
The main drawback of these ...
EPFL2020