scholarly journals iMon

2021 ◽  
Vol 5 (4) ◽  
pp. 1-26
Author(s):  
Sinh Huynh ◽  
Rajesh Krishna Balan ◽  
JeongGil Ko
Keyword(s):  
Mobile Applications ◽  
State Of The Art ◽  
Tracking System ◽  
Tracking Error ◽  
Motion Blur ◽  
Gaze Tracking ◽  
The Gaze ◽  
Previous State ◽  
Personal Productivity ◽  
Different Sources

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to overcome errors caused by microsaccade eye motions that cause the exact gaze point to be uncertain. Second, we design an image enhancement model to refine visual details and remove motion blur effects of input eye images. Finally, we apply a calibration scheme to correct for differences between the perceived and actual gaze points caused by individual Kappa angle differences. With all these improvements, iMon achieves a person-independent per-frame tracking error of 1.49 cm (on smartphones) and 1.94 cm (on tablets) when tested with the GazeCapture dataset and 2.01 cm with the TabletGaze dataset. This outperforms the previous state-of-the-art solutions by ~22% to 28%. By averaging multiple per-frame estimations that belong to the same fixation point and applying personal calibration, the tracking error is further reduced to 1.11 cm (smartphones) and 1.59 cm (tablets). Finally, we built implementations that run on an iPhone 12 Pro and show that our mobile implementation of iMon can run at up to 60 frames per second - thus making gaze-based control of applications possible.

scholarly journals A single-camera gaze tracking system under natural light

2018 ◽  
Vol 11 (4) ◽  
Author(s):  
Feng Xiao ◽  
Dandan Zheng ◽  
Kejie Huang ◽  
Yue Qiu ◽  
Haibin Shen
Keyword(s):  
Tracking System ◽  
Weight Coefficient ◽  
Anchor Point ◽  
Natural Light ◽  
Single Camera ◽  
Gaze Tracking ◽  
The Gaze ◽  
Normalized Error ◽  
Feature Based ◽  
Iris Center

Gaze tracking is a human-computer interaction technology, and it has been widely studied in the academic and industrial fields. However, constrained by the performance of the specific sensors and algorithms, it has not been popularized for everyone. This paper proposes a single-camera gaze tracking system under natural light to enable its versatility. The iris center and anchor point are the most crucial factors for the accuracy of the system. The accurate iris center is detected by the simple active contour snakuscule, which is initialized by the prior knowledge of eye anatomical dimensions. After that, a novel anchor point is computed by the stable facial landmarks. Next, second-order mapping functions use the eye vectors and the head pose to estimate the points of regard. Finally, the gaze errors are improved by implementing a weight coefficient on the points of regard of the left and right eyes. The feature position of the iris center achieves an accuracy of 98.87% on the GI4E database when the normalized error is lower than 0.05. The accuracy of the gaze tracking method is superior to the-state-of-the-art appearance-based and feature-based methods on the EYEDIAP database.

scholarly journals Enhancing Reading Advancement Using Eye Gaze Tracking

2020 ◽  
Vol sceeer (3d) ◽  
pp. 59-64
Author(s):  
Saadaldeen Ahmed ◽  
Mustafa Fadhil ◽  
Salwa Abdulateef
Keyword(s):  
Error Rate ◽  
Tracking System ◽  
Eye Gaze ◽  
Tracking Error ◽  
Physical Characteristics ◽  
Reading Process ◽  
Average Height ◽  
Gaze Tracking ◽  
Baseline Algorithm

This research aims to understand the enhancing reading advancement using eye gaze tracking in regards to pull the increase of time interacting with such devices along. In order to realize that, user should have a good understanding of the reading process and of the eye gaze tracking systems; as well as a good understanding of the issues existing while using eye gaze tracking system for reading process. Some issues are very common, so our proposed implementation algorithm compensate these issues. To obtain the best results possible, two mains algorithm have been implemented: the baseline algorithm and the algorithm to smooth the data. The tracking error rate is calculated based on changing points and missed changing points. In [21], a previous implementation on the same data was done and the final tracking error rate value was of 126%. The tracking error rate value seems to be abnormally high but this value is actually useful as described in [21]. For this system, all the algorithms used give a final tracking error rate value of 114.6%. Three main origins of the accuracy of the eye gaze reading were normal fixation, regression, skip fixation; and accuracies are displayed by the tracking rate value obtained. The three main sources of errors are the calibration drift, the quality of the setup and the physical characteristics of the eyes. For the tests, the graphical interface uses characters with an average height of 24 pixels for the text. By considering that the subject was approximately at 60 centimeters of the tracker. The character on the screen represents an angle of ±0.88◦; which is just above the threshold of ±0.5◦ imposed by the physical characteristics of the eyeball for the advancement of reading using eye gaze tracking.

scholarly journals GAZE TRACKING SYSTEM MODEL BASED ON PHYSICAL PARAMETERS

2007 ◽  
Vol 21 (05) ◽  
pp. 855-877 ◽  
Author(s):  
ARANTXA VILLANUEVA ◽  
RAFAEL CABEZA ◽  
SONIA PORTA
Keyword(s):  
Mathematical Modeling ◽  
Eye Tracking ◽  
Optical Axis ◽  
Tracking System ◽  
System Model ◽  
Physical Parameters ◽  
Visual Axis ◽  
Gaze Tracking ◽  
The Gaze ◽  
The Past

In the past years, research in eye tracking development and applications has attracted much attention and the possibility of interacting with a computer employing just gaze information is becoming more and more feasible. Efforts in eye tracking cover a broad spectrum of fields, system mathematical modeling being an important aspect in this research. Expressions relating to several elements and variables of the gaze tracker would lead to establish geometric relations and to find out symmetrical behaviors of the human eye when looking at a screen. To this end a deep knowledge of projective geometry as well as eye physiology and kinematics are basic. This paper presents a model for a bright-pupil technique tracker fully based on realistic parameters describing the system elements. The system so modeled is superior to that obtained with generic expressions based on linear or quadratic expressions. Moreover, model symmetry knowledge leads to more effective and simpler calibration strategies, resulting in just two calibration points needed to fit the optical axis and only three points to adjust the visual axis. Reducing considerably the time spent by other systems employing more calibration points renders a more attractive model.

A REMOTE GAZE TRACKING SYSTEM USING GRAY-DISTRIBUTION-BASED VIDEO PROCESSING

2012 ◽  
Vol 24 (03) ◽  
pp. 217-227 ◽  
Author(s):  
Xiao-Hui Yang ◽  
Jian-De Sun ◽  
Ju Liu ◽  
Xin-Chao Li ◽  
Cai-Xia Yang ◽  
...  
Keyword(s):  
Video Processing ◽  
Near Infrared ◽  
Tracking System ◽  
Calibration Procedure ◽  
Infrared Light ◽  
Video Frame ◽  
Light Sources ◽  
Gaze Tracking ◽  
Practical Applications ◽  
The Gaze

Gaze tracking has drawn increasing attention and applied wildly in the areas of disabled aids, medical diagnosis, etc. In this paper, a remote gaze tracking system is proposed. The system is video-based, and the video is captured under the illumination of near infrared light sources. Only one camera is employed in the system, which keeps the equipment portable for the users. The corneal glints and the pupil center, whose extraction accuracy determines the performance of the gaze tracking system, are obtained according to the gray distribution of the video frame. And then, the positions of the points on the screen that the user fixating are estimated by the gaze tracking algorithm based on cross-ratio-invariant. Additionally, a calibration procedure is necessary to eliminate the error produced by the deviation of the optical and visual axes. The proposed remote gaze tracking system has a low computational complexity and high robustness, and experiment results indicate that it is tolerant of head movement and still works well for users wearing glasses as well. Besides, the angle error of the gaze tracking system is 0.40 degree of the subjects without glasses, correspondingly, 0.48 degree of the subjects with glasses, which is comparable to most of the existing commercial systems and promising for most of the potential practical applications.

scholarly journals Behavioral Activity Recognition Based on Gaze Ethograms

2020 ◽  
Vol 30 (07) ◽  
pp. 2050025 ◽  
Author(s):  
Javier De Lope ◽  
Manuel Graña
Keyword(s):  
Open Source ◽  
Activity Recognition ◽  
Tracking System ◽  
Video Clip ◽  
Behavioral Research ◽  
Behavioral Activity ◽  
Target Area ◽  
Gaze Tracking ◽  
The Gaze ◽  
Classifier Selection

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user’s behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

scholarly journals The Gaze Tracking System with Natural Head Motion Compensation

Informatica ◽  
2012 ◽  
Vol 23 (1) ◽  
pp. 105-124 ◽  
Author(s):  
Vidas Raudonis ◽  
Agnė Paulauskaitė-Tarasevičienė ◽  
Laura Kižauskienė
Keyword(s):  
Motion Compensation ◽  
Tracking System ◽  
Head Motion ◽  
Gaze Tracking ◽  
The Gaze

scholarly journals Efficient Single-Shot Multi-Object Tracking for Vehicles in Traffic Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6358
Author(s):  
Youngkeun Lee ◽  
Sang-ha Lee ◽  
Jisang Yoo ◽  
Soonchul Kwon
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Object Tracking ◽  
State Of The Art ◽  
Tracking System ◽  
Single Shot ◽  
Essential Information ◽  
Trade Off ◽  
Previous State ◽  
Improved Accuracy

Multi-object tracking is a significant field in computer vision since it provides essential information for video surveillance and analysis. Several different deep learning-based approaches have been developed to improve the performance of multi-object tracking by applying the most accurate and efficient combinations of object detection models and appearance embedding extraction models. However, two-stage methods show a low inference speed since the embedding extraction can only be performed at the end of the object detection. To alleviate this problem, single-shot methods, which simultaneously perform object detection and embedding extraction, have been developed and have drastically improved the inference speed. However, there is a trade-off between accuracy and efficiency. Therefore, this study proposes an enhanced single-shot multi-object tracking system that displays improved accuracy while maintaining a high inference speed. With a strong feature extraction and fusion, the object detection of our model achieves an AP score of 69.93% on the UA-DETRAC dataset and outperforms previous state-of-the-art methods, such as FairMOT and JDE. Based on the improved object detection performance, our multi-object tracking system achieves a MOTA score of 68.5% and a PR-MOTA score of 24.5% on the same dataset, also surpassing the previous state-of-the-art trackers.

scholarly journals POnline: An Online Pupil Annotation Tool Employing Crowd-sourcing and Engagement Mechanisms

2019 ◽  
Vol 6 ◽  
pp. 176-191
Author(s):  
David Gil de Gómez Pérez ◽  
Roman Bednarik
Keyword(s):  
State Of The Art ◽  
Gaze Estimation ◽  
Annotation Tool ◽  
Crowd Sourcing ◽  
Data Set ◽  
Gaze Tracking ◽  
Estimation Algorithms ◽  
The Gaze ◽  
Pupil Center ◽  
Pupil Detection

Pupil center and pupil contour are two of the most important features in the eye-image used for video-based eye-tracking. Well annotated databases are needed in order to allow benchmarking of the available- and new pupil detection and gaze estimation algorithms. Unfortunately, creation of such a data set is costly and requires a lot of efforts, including manual work of the annotators. In addition, reliability of manual annotations is hard to establish with a low number of annotators. In order to facilitate progress of the gaze tracking algorithm research, we created an online pupil annotation tool that engages many users to interact through gamification and allows utilization of the crowd power to create reliable annotations \cite{artstein2005bias}. We describe the tool and the mechanisms employed, and report results on the annotation of a publicly available data set. Finally, we demonstrate an example utilization of the new high-quality annotation on a comparison of two state-of-the-art pupil center algorithms.

Building a Low-Cost Eye-Tracking System

2012 ◽  
Vol 263-266 ◽  
pp. 2399-2402
Author(s):  
Chi Wu Huang ◽  
Zong Sian Jiang ◽  
Wei Fan Kao ◽  
Yen Lin Huang
Keyword(s):  
Eye Tracking ◽  
Test Performance ◽  
Tracking System ◽  
Low Cost ◽  
Least Square ◽  
Tracking Accuracy ◽  
Gaze Tracking ◽  
The Gaze ◽  
Image Capturing ◽  
Future Work

This paper presents the developing of a low-cost eye-tracking system by modifying the commercial-over-the-shelf camera to integrate with the proper-tuned open source drivers and the user-defined application programs. The system configuration is proposed and the gaze-tracking approximated by the least square polynomial mapping is described. Comparisons between other low-cost systems as well as commercial system are provided. Our system obtained the highest image capturing rate of 180 frames per second, and the ISO 9241-Part 9 test performance favored our system, in terms of Response time and Correct response rate. Currently, we are developing gaze-tracking accuracy application. The real time gaze-tracking and the Head Movement Estimation are the issues in future work.

Multi-Resolution Autoregressive Graph-to-Graph Translation for Molecules

2019 ◽  
Author(s):  
Wengong Jin ◽  
Regina Barzilay ◽  
Tommi S Jaakkola
Keyword(s):  
Drug Discovery ◽  
State Of The Art ◽  
Molecular Graph ◽  
Biochemical Properties ◽  
Large Margin ◽  
Previous State ◽  
Translation Methods ◽  
Atom Level ◽  
Precursor Molecules ◽  
Prior State

The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.

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