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Driver's Gaze Estimation Algorithm Based on Deep Learning


Affiliations
1 College of Computer and Communication, Hunan Institute of Engineering,Xiangtan 411104, China., China
 

In traffic safety accidents, 80% of accidents are caused by driver distraction,there is an important mapping relationship between the direction of human sight and the focus of attention,studying the driver's gaze estimation algorithm can reduce the incidence of vehicle traffic accidents.In this paper, a driver's gaze estimation system based on deep learning technology is designed.For human eye feature localization, a multi-level localization method is adopted,firstly, the detection network is used to locate the face position, and then the key points of 68 faces are located based on the face image.In the aspect of the expression of the human eye's line of sight, a method of the expression of the human eye's line of sight based on the central point of the pupil and the direction vector of the human eye's line of sight is adopted,a convolution neural network is used to return the line of sight direction.On the MPIIGaze public data set and self-made data set, the experimental results show that the method can accurately estimate the human eye line of sight.

Keywords

Convolutional neural network,CycleGAN, Face detection, Facial landmark detection,Gaze estimation.
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  • . EC Lee, RP Kang, CW Min, J Park,Robust Gaze Tracking Method for Stereoscopic Virtual Systems,HCI Intelligent Multimodal Interaction Environments, Beijing,China,2007,700–709.

  • . Zhou X R. Research on eye tracking algorithm based on headworn eye tracker,doctoral diss.,Harbin University of Technology, Harbin ,2019.

  • . Ji Q, Yang X J, Real-Time Eye, Gaze, and Face PoseTracking for Monitoring Driver Vigilance, Real-Timelmaging,8(5), 2002,357-377.

  • . Tawari A, Chen K H, Trivedi M M, Where is the driver looking: Analysis of Head,Eye and Iris for Robust Gaze Zone Estimation,IEEE International Conference on Intelligent Transportation Systems, Qing Dao,China,2014,988-994.

  • . Vicente F, Huang Z, Xiong X, etal, Driver Gaze Tracking and Eyes Off the Road Detection System, IEEE Transactions on Itelligent Transportation Systems, 16(4),2015,2014-2027.

  • . Wang Y, Zhao T, Ding X, et al, Head pose-free eye gaze prediction for driver attention study,2017 IEEE International Conference on Big Data and Smart Computing, Jeju,KR,2017,42- 46.

  • . Choi I H, Hong S K, Kim Y G, Real-time categorization of driver's gaze zone using the deep learning techniques, International Conference on Big Data and Smart Computing, Hong Kong,China,2016,143-148.

  • . Deng H, Zhu W, Monocular Free-Head 3D Gaze Tracking with Deep Learning and Geometry Constraints,2017 IEEE International Conference on Computer Vision，Venice, Italy,2017,3162-3171.

  • . Cheng Y, Lu F, Zhang X, Appearance-based gaze estimation via evaluation-guided asymmetric regression,Proceedings of the European Conference on Computer Vision, Munich, Germany, 2018,100-115.

  • . Chen Z L,Design and implementation of fatigue driving detection system based on facial features, master diss.,Xi'an Technological University, Xi'an,2022.

  • . Ma X T, Fei S M, Research on fatigue driving state detection based on facial features and deep learning, Electronic test, 1(11),2021,33-36.

  • . ZhuJ Y, Park T, Isola P, etal, Unpaired Image-to-Image Translation using Cycle-ConsistentAdversarialNetworks,2017 IEEE International Conference on Computer Vision,Venice, Italy,2017,2223–2232.

  • . Lin TP,Girshick R,et al, Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Hawaii US, 2017, 2117-2125.

  • . Zhang H W,HuY,Zou Y J,et al,Fingerspelling Identification for American Sign Language Based on Resnet-18,Int. J. Advanced Networking and Applications,1(13),2021,4816-4820.

  • . ZhenH F,Josef K,Muhammad A,et a, Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks,IEEE Conference on Computer Vision and Pattern Recognition, UT, USA, 2018,2235-2245.

  • . Hu J, Shen L, Sun G, et al, Squeeze-and-excitation networks, IEEE Conference on Computer Vision and Pattern Recognition, UT, USA, 2018,7132-7141.

  • . Yan B,Yu S C, et al,Gaze Estimation Method Based on EOG Signals,IEEE2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, Harbin, China,2016 ,443 -448.

  • . Zhuang Y Y, Zhang Y C, et al , Appearance based gaze estimation using separable convolution neural networks, IEEE 2021 Advanced Information Technology, Electronic and Automation Control Conference ,Chongqing,China, 2021 ,609 -612.


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  • Driver's Gaze Estimation Algorithm Based on Deep Learning

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Authors

DENGAo
College of Computer and Communication, Hunan Institute of Engineering,Xiangtan 411104, China., China
YANGMeng-hao
College of Computer and Communication, Hunan Institute of Engineering,Xiangtan 411104, China., China
ZENG Gui
College of Computer and Communication, Hunan Institute of Engineering,Xiangtan 411104, China., China
LUOYin
College of Computer and Communication, Hunan Institute of Engineering,Xiangtan 411104, China., China
HUYing
College of Computer and Communication, Hunan Institute of Engineering,Xiangtan 411104, China., China

Abstract


In traffic safety accidents, 80% of accidents are caused by driver distraction,there is an important mapping relationship between the direction of human sight and the focus of attention,studying the driver's gaze estimation algorithm can reduce the incidence of vehicle traffic accidents.In this paper, a driver's gaze estimation system based on deep learning technology is designed.For human eye feature localization, a multi-level localization method is adopted,firstly, the detection network is used to locate the face position, and then the key points of 68 faces are located based on the face image.In the aspect of the expression of the human eye's line of sight, a method of the expression of the human eye's line of sight based on the central point of the pupil and the direction vector of the human eye's line of sight is adopted,a convolution neural network is used to return the line of sight direction.On the MPIIGaze public data set and self-made data set, the experimental results show that the method can accurately estimate the human eye line of sight.

Keywords


Convolutional neural network,CycleGAN, Face detection, Facial landmark detection,Gaze estimation.

References