UAV

Sim-to-Real Transfer for Autonomous Navigation

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B2 L5 R5220

This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.

Computer vision: Teaching computers how to see the world

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Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Teaching algorithms to see

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Four hundred hours of video are uploaded to YouTube every minute. Other than using text to search the title, description or tags associated with a video, video content search is limited. In addition to helping users find content more quickly and accurately, the ability to search video content is of paramount importance to video platforms and advertisers. Last year, YouTube was embroiled in controversy after rolling video ads for Coca-Cola and Amazon, among others, were shown before racist and extremist content. Advertising content is a major revenue stream for video platforms like YouTube

KAUST Ph.D. student Matthias Mueller wins award for drone research

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UAV

KAUST Ph.D. student Matthias Mueller, who works in the Image and Video Understanding Lab under the supervision of KAUST Associate Professor Bernard Ghanem, won the Best Paper/Presentation Award at the 2nd International Workshop on Computer Vision for Unmanned Aerial Vehicles (UAVs). UAVs are commonly known as drones. The workshop was held in September 2018 in Germany in the post-proceedings of the 15th European Conference on Computer Vision workshops. Müller's paper entitled "Teaching UAVs to Race: End to End Regression of Agile Controls in Simulation" outlines the research of Müller and his

Beyond self-driving simulations: teaching machines to learn

1 min read ·
Teaching has the power to test the limits of one's knowledge. Teaching algorithms to learn using machine learning is making it possible for cars to do away with human drivers in the near future, but this has also opened up new questions about the limits of our knowledge of the brain and learning. Bernard Ghanem, KAUST associate professor of electrical engineering from the University's Visual Computing Center and principal investigator of the Image and Video Understanding Lab, applies machine learning techniques to computer vision for automated navigation (for example, self-driving cars and

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.