scholarly journals A New Gaze Estimation Method Based on Homography Transformation Derived from Geometric Relationship

2020 ◽  
Vol 10 (24) ◽  
pp. 9079
Author(s):  
Kaiqing Luo ◽  
Xuan Jia ◽  
Hua Xiao ◽  
Dongmei Liu ◽  
Li Peng ◽  
...  
Keyword(s):  
Estimation Method ◽  
Gaze Estimation ◽  
Estimation Model ◽  
Experimental Accuracy ◽  
Unknown Parameters ◽  
The Gaze ◽  
Marquardt Algorithm ◽  
Mapping Equation ◽  
Estimation System ◽  
Homography Transformation

In recent years, the gaze estimation system, as a new type of human-computer interaction technology, has received extensive attention. The gaze estimation model is one of the main research contents of the system. The quality of the model will directly affect the accuracy of the entire gaze estimation system. To achieve higher accuracy even with simple devices, this paper proposes an improved mapping equation model based on homography transformation. In the process of experiment, the model mainly uses the “Zhang Zhengyou calibration method” to obtain the internal and external parameters of the camera to correct the distortion of the camera, and uses the LM(Levenberg-Marquardt) algorithm to solve the unknown parameters contained in the mapping equation. After all the parameters of the equation are determined, the gaze point is calculated. Different comparative experiments are designed to verify the experimental accuracy and fitting effect of this mapping equation. The results show that the method can achieve high experimental accuracy, and the basic accuracy is kept within 0.6∘. The overall trend shows that the mapping method based on homography transformation has higher experimental accuracy, better fitting effect and stronger stability.

scholarly journals 24-Gaze-Point Calibration Method for Improving the Precision of AC-EOG Gaze Estimation

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3650 ◽  
Author(s):  
Muhammad Syaiful Amri bin Suhaimi ◽  
Kojiro Matsushita ◽  
Minoru Sasaki ◽  
Waweru Njeri
Keyword(s):  
Conventional Method ◽  
Affine Transformation ◽  
Estimation Method ◽  
Calibration Method ◽  
Comparative Investigation ◽  
Gaze Estimation ◽  
Angle Error ◽  
The Gaze ◽  
Different Characteristics ◽  
Gaze Position

This paper sought to improve the precision of the Alternating Current Electro-Occulo-Graphy (AC-EOG) gaze estimation method. The method consisted of two core techniques: To estimate eyeball movement from EOG signals and to convert signals from the eyeball movement to the gaze position. In conventional research, the estimations are computed with two EOG signals corresponding to vertical and horizontal movements. The conversion is based on the affine transformation and those parameters are computed with 24-point gazing data at the calibration. However, the transformation is not applied to all the 24-point gazing data, but to four spatially separated data (Quadrant method), and each result has different characteristics. Thus, we proposed the conversion method for 24-point gazing data at the same time: To assume an imaginary center (i.e., 25th point) on gaze coordinates with 24-point gazing data and apply an affine transformation to 24-point gazing data. Then, we conducted a comparative investigation between the conventional method and the proposed method. From the results, the average eye angle error for the cross-shaped electrode attachment is x = 2.27 ° ± 0.46 ° and y = 1.83 ° ± 0.34 ° . In contrast, for the plus-shaped electrode attachment, the average eye angle error is is x = 0.94 ° ± 0.19 ° and y = 1.48 ° ± 0.27 ° . We concluded that the proposed method offers a simpler and more precise EOG gaze estimation than the conventional method.

scholarly journals CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Warapon Chinsatit ◽  
Takeshi Saitoh
Keyword(s):  
Medical Diagnosis ◽  
Good Accuracy ◽  
Detection Method ◽  
Input Image ◽  
Classification Model ◽  
Gaze Estimation ◽  
The Gaze ◽  
Pupil Center ◽  
Center Position ◽  
Estimation System

This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user’s eye can be used in various applications, such as human-computer interaction, medical diagnosis, and psychological studies. However, users tend to blink frequently; thus, estimating gaze direction is difficult. The proposed method uses two CNN models. The first CNN model is used to classify the eye state and the second is used to estimate the pupil center position. The classification model filters images with closed eyes and terminates the gaze estimation process when the input image shows a closed eye. In addition, this paper presents a process to create an eye image dataset using a wearable camera. This dataset, which was used to evaluate the proposed method, has approximately 20,000 images and a wide variation of eye states. We evaluated the proposed method from various perspectives. The result shows that the proposed method obtained good accuracy and has the potential for application in wearable device-based gaze estimation.

scholarly journals Continuous Driver’s Gaze Zone Estimation Using RGB-D Camera

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1287 ◽  
Author(s):  
Yafei Wang ◽  
Guoliang Yuan ◽  
Zetian Mi ◽  
Jinjia Peng ◽  
Xueyan Ding ◽  
...  
Keyword(s):  
Real World ◽  
Estimation Method ◽  
Feature Space ◽  
Image Feature ◽  
Gaze Estimation ◽  
Head Pose ◽  
Pose Tracking ◽  
Angle Space ◽  
Long Time ◽  
Estimation System

The driver gaze zone is an indicator of a driver’s attention and plays an important role in the driver’s activity monitoring. Due to the bad initialization of point-cloud transformation, gaze zone systems using RGB-D cameras and ICP (Iterative Closet Points) algorithm do not work well under long-time head motion. In this work, a solution for a continuous driver gaze zone estimation system in real-world driving situations is proposed, combining multi-zone ICP-based head pose tracking and appearance-based gaze estimation. To initiate and update the coarse transformation of ICP, a particle filter with auxiliary sampling is employed for head state tracking, which accelerates the iterative convergence of ICP. Multiple templates for different gaze zone are applied to balance the templates revision of ICP under large head movement. For the RGB information, an appearance-based gaze estimation method with two-stage neighbor selection is utilized, which treats the gaze prediction as the combination of neighbor query (in head pose and eye image feature space) and linear regression (between eye image feature space and gaze angle space). The experimental results show that the proposed method outperforms the baseline methods on gaze estimation, and can provide a stable head pose tracking for driver behavior analysis in real-world driving scenarios.

Visualization Method Corresponding to Regression Problems and Its Application to Deep Learning-Based Gaze Estimation Model

2020 ◽  
Vol 24 (5) ◽  
pp. 676-684
Author(s):  
Daigo Kanda ◽  
◽  
Shin Kawai ◽  
Hajime Nobuhara
Keyword(s):  
Deep Learning ◽  
Personal Information ◽  
Smart Devices ◽  
Gaze Estimation ◽  
Visualization Method ◽  
Massachusetts Institute Of Technology ◽  
Estimation Model ◽  
Regression Problem ◽  
The Gaze ◽  
Institute Of Technology

The human gaze contains substantial personal information and can be extensively employed in several applications if its relevant factors can be accurately measured. Further, several fields could be substantially innovated if the gaze could be analyzed using popular and familiar smart devices. Deep learning-based methods are robust, making them crucial for gaze estimation on smart devices. However, because internal functions in deep learning are black boxes, deep learning systems often make estimations for unclear reasons. In this paper, we propose a visualization method corresponding to a regression problem to solve the black box problem of the deep learning-based gaze estimation model. The proposed visualization method can clarify which region of an image contributes to deep learning-based gaze estimation. We visualized the gaze estimation model proposed by a research group at the Massachusetts Institute of Technology. The accuracy of the estimation was low, even when the facial features important for gaze estimation were recognized correctly. The effectiveness of the proposed method was further determined through quantitative evaluation using the area over the MoRF perturbation curve (AOPC).

scholarly journals A Novel Method for Estimating Free Space 3D Point-of-Regard Using Pupillary Reflex and Line-of-Sight Convergence Points

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2292 ◽  
Author(s):  
Zijing Wan ◽  
Xiangjun Wang ◽  
Kai Zhou ◽  
Xiaoyun Chen ◽  
Xiaoqing Wang
Keyword(s):  
Free Space ◽  
Estimation Method ◽  
Measurement Model ◽  
Line Of Sight ◽  
Gaze Estimation ◽  
Gaze Tracking ◽  
The Gaze ◽  
Convergence Point ◽  
Spatial Coordinates ◽  
Tracking Device

In this paper, a novel 3D gaze estimation method for a wearable gaze tracking device is proposed. This method is based on the pupillary accommodation reflex of human vision. Firstly, a 3D gaze measurement model is built. By uniting the line-of-sight convergence point and the size of the pupil, this model can be used to measure the 3D Point-of-Regard in free space. Secondly, a gaze tracking device is described. By using four cameras and semi-transparent mirrors, the gaze tracking device can accurately extract the spatial coordinates of the pupil and eye corner of the human eye from images. Thirdly, a simple calibration process of the measuring system is proposed. This method can be sketched as follows: (1) each eye is imaged by a pair of binocular stereo cameras, and the setting of semi-transparent mirrors can support a better field of view; (2) the spatial coordinates of the pupil center and the inner corner of the eye in the images of the stereo cameras are extracted, and the pupil size is calculated with the features of the gaze estimation method; (3) the pupil size and the line-of-sight convergence point when watching the calibration target at different distances are computed, and the parameters of the gaze estimation model are determined. Fourthly, an algorithm for searching the line-of-sight convergence point is proposed, and the 3D Point-of-Regard is estimated by using the obtained line-of-sight measurement model. Three groups of experiments were conducted to prove the effectiveness of the proposed method. This approach enables people to obtain the spatial coordinates of the Point-of-Regard in free space, which has great potential in the application of wearable devices.

scholarly journals Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Keiko Sakurai ◽  
Mingmin Yan ◽  
Koichi Tanno ◽  
Hiroki Tamura
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Estimation Method ◽  
Gaze Estimation ◽  
Strongly Correlated ◽  
Kinect Sensor ◽  
Tracking Method ◽  
Estimation System ◽  
Communication Methods ◽  
Very High

A gaze estimation system is one of the communication methods for severely disabled people who cannot perform gestures and speech. We previously developed an eye tracking method using a compact and light electrooculogram (EOG) signal, but its accuracy is not very high. In the present study, we conducted experiments to investigate the EOG component strongly correlated with the change of eye movements. The experiments in this study are of two types: experiments to see objects only by eye movements and experiments to see objects by face and eye movements. The experimental results show the possibility of an eye tracking method using EOG signals and a Kinect sensor.

Homography-Based Low-Cost Gaze Estimation and its Application to the Usability Assessment of Digital Prototypes of Information Appliances

2013 ◽  
Author(s):  
Satoshi Kanai ◽  
Hiroaki Date
Keyword(s):  
Low Cost ◽  
Test Session ◽  
Gaze Estimation ◽  
Ui Design ◽  
The Gaze ◽  
Usability Assessment ◽  
Simple Event ◽  
Information Appliances ◽  
Estimation System ◽  
Digital Prototype

Recently 3D digital prototypes of information appliances have been proposed for efficient user acceptance tests of user-interface (UI) usability. The purpose of this research is to develop a gaze estimation system based on Homography and to fully integrate it with a 3D digital prototype of the information appliances in order to obtain information more useful for usability assessment. The estimation system consists only of four infrared LEDs and a USB camera and is low-cost. The gaze estimation enables the system not only to record a gaze point on the prototype but to identify the UI objects which the user is looking for in real time during the test session. A gaze-based index was newly introduced to identify the misleading UI objects and to quantify the irrelevance of the UI design. A case study suggested that the integration of the gaze estimation with the 3D digital prototype and the proposed index were useful for automatically identifying which irrelevant UI objects misled the users’ operations which could not yet be captured in previous simple event logging of the user inputs.

scholarly journals Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9068
Author(s):  
Mohd Faizan Ansari ◽  
Pawel Kasprowski ◽  
Marcin Obetkal
Keyword(s):  
Neural Networks ◽  
Estimation Method ◽  
Infrared Light ◽  
Gaze Estimation ◽  
Reasonable Accuracy ◽  
High Quality ◽  
Gaze Tracking ◽  
The Gaze ◽  
Web Camera ◽  
Additional Hardware

Gaze estimation plays a significant role in understating human behavior and in human–computer interaction. Currently, there are many methods accessible for gaze estimation. However, most approaches need additional hardware for data acquisition which adds an extra cost to gaze tracking. The classic gaze tracking approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are fundamentally based on the characteristics of the eye region, utilizing infrared light and iris glint to track the gaze point. It requires high-quality images with particular environmental conditions and another light source. Recent studies on appearance-based gaze estimation have demonstrated the capability of neural networks, especially convolutional neural networks (CNN), to decode gaze information present in eye images and achieved significantly simplified gaze estimation. In this paper, a gaze estimation method that utilizes a CNN for gaze estimation that can be applied to various platforms without additional hardware is presented. An easy and fast data collection method is used for collecting face and eyes images from an unmodified desktop camera. The proposed method registered good results; it proves that it is possible to predict the gaze with reasonable accuracy without any additional tools.

Estimation of Forest Biomass Energy Resources Based on Bottom-up Approach in China

2015 ◽  
Vol 8 (1) ◽  
pp. 272-275
Author(s):  
Lan Zhang ◽  
Dan Yu ◽  
Caihong Zhang ◽  
Weidong Zhang
Keyword(s):  
Forest Biomass ◽  
Estimation Method ◽  
Energy Resource ◽  
Biomass Energy ◽  
Energy Resources ◽  
Theoretical Ground ◽  
Estimation Model ◽  
Bottom Up ◽  
Rational Development ◽  
Calculation Results

Currently, the forest biomass energy development is at an initial stage and the estimation method for the forest biomass energy resource reserve is to be unified and refined although there is a great value and potential in the development and utilization of forest biomass energy in China. Based on the existing studies, the present paper analyzes the origins and types of forest biomass energy resources in the perspective of sustainable forestry management, constructs the estimation model using a bottom-up approach, and estimates the total existing forest biomass energy resource reserve in China based on the data of the 7th Forest Resource Survey. The estimation method and the calculation results provide the important theoretical ground for promoting the rational development of forest biomass energy in China.

scholarly journals Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 26
Author(s):  
David González-Ortega ◽  
Francisco Javier Díaz-Pernas ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Virtual Reality ◽  
Comparative Analysis ◽  
Driving Simulator ◽  
Estimation Method ◽  
Estimation Methods ◽  
Gaze Estimation ◽  
Driving Simulators ◽  
Oculus Rift ◽  
Gaze Patterns ◽  
And Performance

Driver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

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