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Attention Mechanism based Real Time Gaze Tracking in Natural Scenes with Residual Blocks
Dai LH(戴立红)1,2,3,4; Liu JG(刘金国)1,2; Gao Y(高扬)6
Department空间自动化技术研究室
Source PublicationIEEE Transactions on Cognitive and Developmental Systems
ISSN2379-8920
2021
Pages1-11
Indexed ByEI
EI Accession number20211210103049
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China under Grant 2018YFB1304600 ; Natural Science Foundation of China (Grant 51775541,51575412,52075530) ; CAS Interdisciplinary Innovation Team under Grant JCTD-2018-11 ; AiBle project co-financed by the European Regional Development Fund
KeywordGaze tracking attention mechanism residual blocks CNN
Abstract

Gaze tracking is widely used in fatigue driving detection, eye disease diagnosis, mental illness diagnosis, website or advertising design, virtual reality, gaze-control devices and human-computer interaction. However, the influence of light, specular reflection and occlusion, the change of head pose, especially the ever-changing human pose in natural scenes, have brought great challenges to the accurate gaze tracking. In this paper, gaze tracking in natural scenes is studied, and a method based on Convolutional Neural Network (CNN) with residual blocks is proposed, in which attention mechanism is integrated into the network to improve the accuracy of gaze tracking. Furthermore, it is tested on the GazeFollow database which contains six kinds of databases. The results show that the performance of proposed method outperforms that of other state-of-the-art methods in natural scenes. Moreover, the proposed method has better real-time performance and is more suitable for practical applications.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28634
Collection空间自动化技术研究室
Corresponding AuthorLiu JG(刘金国)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of the Chinese Academy of Sciences, Beijing 100049, China
4.School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China
5.School of Computing, University of Portsmouth, Portsmouth P01 3HE, U.K.
6.Space Technology for Autonomous and Robotic Systems Laboratory (STAR LAB), Surrey Space Centre, University of Surrey, Guildford GU2 7XH, U.K.
Recommended Citation
GB/T 7714
Dai LH,Liu JG,Gao Y. Attention Mechanism based Real Time Gaze Tracking in Natural Scenes with Residual Blocks[J]. IEEE Transactions on Cognitive and Developmental Systems,2021:1-11.
APA Dai LH,Liu JG,&Gao Y.(2021).Attention Mechanism based Real Time Gaze Tracking in Natural Scenes with Residual Blocks.IEEE Transactions on Cognitive and Developmental Systems,1-11.
MLA Dai LH,et al."Attention Mechanism based Real Time Gaze Tracking in Natural Scenes with Residual Blocks".IEEE Transactions on Cognitive and Developmental Systems (2021):1-11.
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