Mobile Eye Tracking

Mobile eye tracking uses smartphone or wearable device cameras to estimate gaze direction, aiming to provide a low-cost, accessible alternative to traditional eye trackers. Current research focuses on improving accuracy, particularly for dynamic visual stimuli, using deep learning models like convolutional and recurrent neural networks (CNNs and RNNs), and optimizing these models for resource-constrained mobile devices through techniques such as model quantization and pruning. This technology has significant implications for various fields, including usability testing, vision research, and the development of augmented reality interfaces, by enabling large-scale studies and personalized user experiences. Privacy-preserving techniques are also a growing area of focus.