Driver's gaze zone estimation by transfer learning

Tayibnapis, Iman Rahmansyah; Choi, Min-Kook; Kwon, Soon Jae

doi:10.1109/icce.2018.8326308

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…However, the processing time was prolonged, particularly because the driver's gaze position was calculated from the extracted features using three VGG-face 16 models. In [18], a driver's gaze position was calculated by transfer learning using a trained model. For the trained model, the iTracker CNN, used by iTracker Team in Apple phones and tablets to estimate gaze regions, was used.…”

Section: ) Deep Feature-based Methodsmentioning

confidence: 99%

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CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments

Yoon

Park

2020

IEEE Access

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Recently, the need for research on tracking driver gazes is increasing owing to the development of driver convenience systems, such as autonomous driving or intelligent driver monitoring system, to address traffic accidents caused by negligence. A camera is installed in a vehicle to track the driver's gaze in vehicle environment. The accuracy of estimating driver gazes in vehicle environments reduces if a motion blur of the driver occurs, owing to vehicular vibrations during driving. Most past studies on gaze-tracking of a driver in a vehicle did not consider the motion blurs in their experiments. To address this concern, we propose a method for improving the accuracy of gaze estimation by deblurring the blurred images of a driver from the vehicle. This study is the first attempt to calculate a driver's gaze by deblurring a motion blurred image with CycleGAN, whereas simultaneously using the image information from the two cameras in the vehicle. In previous studies, multiple deep CNNs were used for obtaining the images of a driver's eyes and face. In this study, information obtained from the two cameras in the vehicle are integrated into an image with three channels and thereafter deblurred, consequently reducing the time required for training. Whereas in previous studies the gaze position was not calculated for severe blurs by measuring the level of blur from the input image, the gaze position was calculated for all the input images in this study. From the database (Dongguk blurred gaze database (DBGD)) from 26 drivers in actual vehicles and the Columbia gaze dataset (CAVE-DB) that is an open database, the proposed method exhibited greater accuracy than the existing methods.

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Section: ) Deep Feature-based Methodsmentioning

confidence: 99%

“…-Depends on the detection of facial landmarks [1,2,[4][5][6][7]9,10] Deep feature-based Using AlexNet, VGG net-16 [11], AlexNet [14], SqueezeNet [15], VGG-face 16 [17], iTracker model [18] The accuracy of gaze detection is scarcely reduced owing to the rotation of the driver's face.…”

Section: Handcrafted Featurebasedmentioning

confidence: 99%

CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments

Yoon

Park

2020

IEEE Access

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“…2, many researchers have attempted to develop contactless monitoring systems for various applications. Previous methods for monitoring people can be divided into 1) vision-based [4][5][6][7][8][9][10][11][12][13], 2) wearable-based [14][15][16][17][18][19][20][21], and 3) head-gaze-based technologies [22][23][24][25][26][27][28][29][30][31]. Vision-based methods usually detect and track the pose or movements of medically vulnerable people using user images captured by cameras.…”

Section: Figure 1: Examples Of Medically Vulnerable Peoplementioning

confidence: 99%

“…For example, head-gaze estimation systems estimate where a user is looking (e.g., a side or rear mirror, etc.) while driving a vehicle [22][23][24][25][26][27][28][29][30][31] (refer to Fig. 3b).…”

Section: Figure 1: Examples Of Medically Vulnerable Peoplementioning

confidence: 99%

Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

Kim¹,

Jeong²

2022

Computers, Materials &Amp; Continua

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Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users' continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user's head orientation into the gaze direction. The proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned conditions. To this end, we implemented a deep learning pipeline that can handle different types and combinations of data. The proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze estimators. Through various experiments, we found that 1) an infrared-modality provides more useful features than a depth-modality, 2) multi-view multi-modal approaches provide better accuracy than singleview single-modal approaches, and 3) the proposed estimators achieve a high inference efficiency that can be used in real-time applications.

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“…Tawari et al [7] used a multiple camera vision algorithm to monitor and detect driver behavior in a variety of occlusion and lighting conditions. Additional studies have been conducted to develop models to analyze head motion using video capture methods, as seen in [8]- [10]. While video methods enable a high degree of visual clarity for processing driver gaze, the privacy concern of being recorded is undesirable.…”

Section: Introductionmentioning

confidence: 99%

Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

Bresnahan

2021

IEEE Access

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Eight different driver head movements are measured using a millimeter-wave FMCW radar mounted in the dashboard of a car. The micro-Doppler signatures are converted into a spectrogram image format for analysis and classification purposes. The eight different head motions exhibit unique timefrequency profiles, which can be classified by deep learning algorithms. In this study, a convolutional neural network is used to classify the eight head motions with an optimized window size. Various dataset permutations are considered, such as the effect of window width on classification accuracy and the classification accuracy of head motions in a still car compared to a moving car.

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Driver's gaze zone estimation by transfer learning

Cited by 10 publications

References 13 publications

CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments

CycleGAN-Based Deblurring for Gaze Tracking in Vehicle Environments

Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

Classification of Driver Head Motions Using a mm-Wave FMCW Radar and Deep Convolutional Neural Network

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