Self-calibrated driver gaze estimation via gaze pattern learning

Yuan, Guoliang; Yan, Huizhu; Fu, Xianping

doi:10.1016/j.knosys.2021.107630

Cited by 15 publications

(12 citation statements)

References 28 publications

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“…Here, the parameters of GPR are similar to Ref. [ 9 ]. The PLSR method trains the regression model on the principle component by projecting the variables into new space.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…The GPM method builds the mapping relationship as performed in Ref. [ 9 ], and the HTP method treats the gaze point refinement as a homography transform from the primary image plane to the final image plane.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…At this point, it is also practical to build an online model as Ref. [ 9 ]. The refinement method works on the online model.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Yang et al [ 5 ] utilized a non-linear polynomial method to build the mapping relationship linking the features to the gaze points. Yuan et al [ 9 ] used six gaze zones related to driver gaze glance as calibration points. This extracted and clustered more representative gaze features to build the regression by GPR.…”

Section: Related Workmentioning

confidence: 99%

“…Manual gaze mapping methods necessarily require driver compliance [ 5 , 6 ] and human cooperation before each session, which adds significant inconvenience for driver eye gaze tracking systems. Several automatic mapping methods have approached driver gaze zone estimation utilizing gaze behavior or gaze frequency on specific driving data [ 7 , 8 , 9 ], which are heavily dependent on the parameter estimation accuracy and fragile to the outliers. This makes the driver gaze estimation system session-exclusive or person-exclusive, and requires re-calibration or re-mapping in the setup procedure before each session, which seriously affects the applicability of the pre-trained gaze model.…”

Section: Introductionmentioning

confidence: 99%

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Dual-Cameras-Based Driver’s Eye Gaze Tracking System with Non-Linear Gaze Point Refinement

Ding

Yuan

2022

Sensors

Self Cite

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The human eye gaze plays a vital role in monitoring people’s attention, and various efforts have been made to improve in-vehicle driver gaze tracking systems. Most of them build the specific gaze estimation model by pre-annotated data training in an offline way. These systems usually tend to have poor generalization performance during the online gaze prediction, which is caused by the estimation bias between the training domain and the deployment domain, making the predicted gaze points shift from their correct location. To solve this problem, a novel driver’s eye gaze tracking method with non-linear gaze point refinement is proposed in a monitoring system using two cameras, which eliminates the estimation bias and implicitly fine-tunes the gaze points. Supported by the two-stage gaze point clustering algorithm, the non-linear gaze point refinement method can gradually extract the representative gaze points of the forward and mirror gaze zone and establish the non-linear gaze point re-mapping relationship. In addition, the Unscented Kalman filter is utilized to track the driver’s continuous status features. Experimental results show that the non-linear gaze point refinement method outperforms several previous gaze calibration and gaze mapping methods, and improves the gaze estimation accuracy even on the cross-subject evaluation. The system can be used for predicting the driver’s attention.

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“…Here, the parameters of GPR are similar to Ref. [ 9 ]. The PLSR method trains the regression model on the principle component by projecting the variables into new space.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

“…At this point, it is also practical to build an online model as Ref. [ 9 ]. The refinement method works on the online model.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%