Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Head-mounted eye tracking has significant potential for gaze-based applications such as life logging, mental health monitoring, or the quantified self. A neglected challenge for the long-term recordings required by these applications is that drift in the initial person-specific eye tracker calibration, for example caused by physical activity, can severely impact gaze estimation accuracy and thus system performance and user experience.  first analyse calibration drift on a new dataset of natural gaze data recorded using synchronised video-based and Electrooculography-based eye trackers of 20 users performing everyday activities in a mobile setting. Based on this analysis  present a method to automatically self-calibrate head-mounted eye trackers based on a computational model of bottom-up visual saliency. Through evaluations on the dataset  show that our method 1) is effective in reducing calibration drift in calibrated eye trackers and 2) given sufficient data, can achieve gaze estimation accuracy competitive with that of a calibrated eye tracker, without any manual calibration.
['Yusuke Sugano', 'Andreas Bulling']
I am writing a research paper on a idea Self-Calibrating Head-Mounted Eye Trackers Using Egocentric Visual Saliency
[{"from": "human", "value": "I am writing a research paper on a idea Self-Calibrating Head-Mounted Eye Trackers Using Egocentric Visual Saliency"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Head-mounted eye tracking has significant potential for gaze-based applications such as life logging, mental health monitoring, or the quantified self. A neglected challenge for the long-term recordings required by these applications is that drift in the initial person-specific eye tracker calibration, for example caused by physical activity, can severely impact gaze estimation accuracy and thus system performance and user experience.  first analyse calibration drift on a new dataset of natural gaze data recorded using synchronised video-based and Electrooculography-based eye trackers of 20 users performing everyday activities in a mobile setting. Based on this analysis  present a method to automatically self-calibrate head-mounted eye trackers based on a computational model of bottom-up visual saliency. Through evaluations on the dataset  show that our method 1) is effective in reducing calibration drift in calibrated eye trackers and 2) given sufficient data, can achieve gaze estimation accuracy competitive with that of a calibrated eye tracker, without any manual calibration."}]