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A Deep Learning Investigation of Eye Movements and Drone State Estimation in Racing
Deep Learning Eye Movements and State Estimation
Drones have become increasingly popular in recent years, and drone racing has become a popular sport. However, little is known about how drone pilots use their eyes to extract relevant visual information from the video stream in order to control the drone. This project aims to investigate the relationship between eye gaze, optical flow, and drone state estimation and piloting behavior using statistical modeling and deep learning techniques. The successful student will gain experience in statistical modeling, machine learning, and data visualization, and will have the opportunity to make a significant contribution to the field of drone racing.
Drones have become increasingly popular in recent years, and drone racing has become a popular sport. However, little is known about how drone pilots use their eyes to extract relevant visual information from the video stream in order to control the drone. This project aims to investigate the relationship between eye gaze, optical flow, and drone state estimation and piloting behavior using statistical modeling and deep learning techniques. The successful student will gain experience in statistical modeling, machine learning, and data visualization, and will have the opportunity to make a significant contribution to the field of drone racing.
The goal of this project is to investigate how eye movements affect drone state estimation and piloting behavior by analyzing a large dataset of eyetracking and optical flow data from human pilots in a drone race. The student will use statistical methods, such as general linear mixed models, machine learning techniques, including LSTM and deep learning, and data visualization techniques to clarify the relationship between optical flow, eye gaze, and piloting behavior in various drone racing maneuvers.
**Requirements:** The successful student will possess strong programming skills in Python and have a background in machine learning and statistics. Previous experience with optical flow and eyetracking is a plus, but not required. Additionally, the student should be able to work independently and have strong communication skills.
The goal of this project is to investigate how eye movements affect drone state estimation and piloting behavior by analyzing a large dataset of eyetracking and optical flow data from human pilots in a drone race. The student will use statistical methods, such as general linear mixed models, machine learning techniques, including LSTM and deep learning, and data visualization techniques to clarify the relationship between optical flow, eye gaze, and piloting behavior in various drone racing maneuvers.
**Requirements:** The successful student will possess strong programming skills in Python and have a background in machine learning and statistics. Previous experience with optical flow and eyetracking is a plus, but not required. Additionally, the student should be able to work independently and have strong communication skills.
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch) and Mathias Gehrig (mgehrig AT ifi DOT uzh DOT ch).
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch) and Mathias Gehrig (mgehrig AT ifi DOT uzh DOT ch).