Eye Tracking
Eye tracking, the technology of monitoring eye movements to understand visual attention and cognitive processes, aims to accurately and efficiently measure gaze direction and pupil characteristics. Current research focuses on developing robust and user-friendly systems using various approaches, including deep learning models (e.g., convolutional neural networks, recurrent neural networks, and transformers) applied to both traditional frame-based and novel event-based camera data. These advancements are driving improvements in accuracy, speed, and power efficiency, with significant implications for diverse fields such as human-computer interaction, medical diagnostics (e.g., assessing neurodegenerative diseases), and the understanding of human perception and cognition.
Papers
Evaluating Image-Based Face and Eye Tracking with Event Cameras
Khadija Iddrisu, Waseem Shariff, Noel E. OConnor, Joseph Lemley, Suzanne Little
Understanding cyclists' perception of driverless vehicles through eye-tracking and interviews
Siri Hegna Berge, Joost de Winter, Dimitra Dodou, Amir Pooyan Afghari, Eleonora Papadimitriou, Nagarjun Reddy, Yongqi Dong, Narayana Raju, Haneen Farah