Abstract
Gaze tracking is the technology that identifies a region in space that a user is looking at. Most previous non-wearable gaze tracking systems use a near-infrared (NIR) light camera with an NIR illuminator. Based on the kind of camera lens used, the viewing angle and depth-of-field (DOF) of a gaze tracking camera can be different, which affects the performance of the gaze tracking system. Nevertheless, to our best knowledge, most previous researches implemented gaze tracking cameras without ground truth information for determining the optimal viewing angle and DOF of the camera lens. Eye-tracker manufacturers might also use ground truth information, but they do not provide this in public. Therefore, researchers and developers of gaze tracking systems cannot refer to such information for implementing gaze tracking system. We address this problem providing an empirical study in which we design an optimal gaze tracking camera based on experimental measurements of the amount and velocity of user’s head movements. Based on our results and analyses, researchers and developers might be able to more easily implement an optimal gaze tracking system. Experimental results show that our gaze tracking system shows high performance in terms of accuracy, user convenience and interest.
Highlights
Gaze tracking has recently become a very active research field that has applications in many different fields, including human computer interfaces (HCI), virtual reality (VR), driver monitoring, eye disease diagnosis, and intelligent machine interfaces
Wein calculate position in the camera model and the z-distance measured by the ultrasonic sensor.weby reliability of on measured head movement our experiments
The lens DOF for our gaze tracking camera was designed to work in the range from 50 to 80 cm, The lens DOF for our gaze tracking camera was designed to work in the range from 50 to 80 based on the results shown in Figure 7e, about 88.3% of user’s z-distances can be covered by our gaze cm, based on the results shown in Figure 7e, about 88.3% of user’s z-distances can be covered by tracking camera
Summary
Gaze tracking has recently become a very active research field that has applications in many different fields, including human computer interfaces (HCI), virtual reality (VR), driver monitoring, eye disease diagnosis, and intelligent machine interfaces. Gaze tracking systems can find the position a user is looking at by using image processing and computer vision technologies. Various kinds of gaze tracking devices, wearable and non-wearable and with single or multiple cameras [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] have been researched. Most non-wearable gaze tracking systems are designed for computer users in a desktop environment using a near-infrared (NIR) camera and an NIR illuminator. Available online: http://www.cs.iit.edu/~agam/cs512/lectnotes/opencv-intro/opencv-intro.html (accessed on 11 May 2016). Available online: http://www.ptgrey.com/Cameras (accessed on 11 May 2016). Available online: https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule (accessed on 2 June 2016)
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