scholarly journals Compensation Method of Natural Head Movement for Gaze Tracking System Using an Ultrasonic Sensor for Distance Measurement

Sensors ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 110 ◽  
Author(s):  
Dongwook Jung ◽  
Jong Lee ◽  
Su Gwon ◽  
Weiyuan Pan ◽  
Hyeon Lee ◽  
...  
Keyword(s):  
Head Movement ◽  
Tracking System ◽  
Distance Measurement ◽  
Ultrasonic Sensor ◽  
Compensation Method ◽  
Gaze Tracking

The Research on Eye Tracking for Gaze Tracking System

2010 ◽  
Vol 36 (8) ◽  
pp. 1051-1061 ◽  
Author(s):  
Chuang ZHANG ◽  
Jian-Nan CHI ◽  
Zhao-Hui ZHANG ◽  
Zhi-Liang WANG
Keyword(s):  
Eye Tracking ◽  
Tracking System ◽  
Gaze Tracking

scholarly journals Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

2021 ◽  
Vol 11 (2) ◽  
pp. 851
Author(s):  
Wei-Liang Ou ◽  
Tzu-Ling Kuo ◽  
Chin-Chieh Chang ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Tracking System ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Tracking Errors ◽  
Gaze Tracking ◽  
Detection Technology ◽  
Pupil Tracking

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

An adaptive blink detector to initialize and update a view-basedremote eye gaze tracking system in a natural scenario

2009 ◽  
Vol 30 (12) ◽  
pp. 1144-1150 ◽  
Author(s):  
Diego Torricelli ◽  
Michela Goffredo ◽  
Silvia Conforto ◽  
Maurizio Schmid
Keyword(s):  
Tracking System ◽  
Eye Gaze ◽  
Gaze Tracking

scholarly journals Wireless UGV for Target Tracking and Obstacle Detection using Live Surveillance

2019 ◽  
Vol 8 (6S2) ◽  
pp. 384-386
Keyword(s):  
Target Tracking ◽  
Distance Measurement ◽  
Obstacle Detection ◽  
Ultrasonic Sensor ◽  
Prototype Model ◽  
Live Video ◽  
Ip Camera ◽  
Complete Set ◽  
Set Up

our aim is to develop a project that will benefit society. But nowadays it is employed to assist human in surveillance, rescue and recovery missions. This paper presents the prototype model of an UGV which is operated wirelessly through manual navigation commands based on the live video captured from the IP camera mounted on the board. The distance measurement is done by the Ultrasonic sensor from the obstacle and displayed in the LCD. The target tracking as well as attacking is done based on the obstacle and environment situation monitored in the live video. This complete set up and working of the UGV is described further in this paper

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