Driver-car Natural Interaction Method Based on Head-Eye Behaviors

Li, Haojie; Zhao, Qijie; Zhao, Weichi; Wu, Yijing

doi:10.1109/ihmsc.2019.00045

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The face detection process involves collecting video footage using the front camera, which is then divided into segments based on time series [14][15][16][17][18][19] . The pre-processed image is used to extract facial feature points in each frame using Dlib and OpenCV, allowing for external features of the face contour to be retrieved and analyzed.…”

Section: A Dlib Library Tracks Key Points Of Facesmentioning

confidence: 99%

Face Fatigue Feature Detection Based on Improved D-S Model in Complex Scenes

Cai,

Liao,

Bai

et al. 2023

IEEE Access

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Fatigued driving is one of the main causes of road traffic accidents. In the process of fatigued driving detection, the evaluation based on a single sign is biased. To improve the adaptability and accuracy of fatigued driving detection, this paper proposes an improved D-S evidence theory-based algorithm for detecting facial fatigue signs. This algorithm uses the multi-thread-optimized Dlib to track and locate the image of the face, captures the 68 key points of the driver's face with reference to the Dlib open-source library, and narrows the target areas to the eyes and mouth regions. Based on the video stream, it calculates the horizontal and vertical ratios of the eyes and mouth to determine the fatigue sign subsets based on the EAR and MAR within a unit cycle, and calculates the Pitch angle after converting the head pose from 2D images to 3D models, which is used for determining the status of the head pose. The algorithm then fuses multiple feature subsets and uses the improved D-S evidence theory to optimize the weights of the three subsets to mitigate the influence of the "general conflict" and "one veto" problems, and to increase the temporal correlation coefficient of a single fatigue sign. Experimental results show that the improved D-S face detection algorithm can effectively solve the problems of lighting and partial occlusion in complex environments, with an accuracy rate of 93.8% for detecting facial fatigue signs.

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Section: A Dlib Library Tracks Key Points Of Facesmentioning

confidence: 99%

Face Fatigue Feature Detection Based on Improved D-S Model in Complex Scenes

Cai,

Liao,

Bai

et al. 2023

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%

“…As stated in the Introduction, head-gaze is a proxy of user gaze direction computed based on the head orientation of users, a useful indicator that does not require precise calibration to represent the interests and intentions of users [22]. Previous studies on the estimation of headgaze mainly focused on monitoring the status of users (e.g., whether the user is concentrating) while driving [22][23][24][25][26][27][28][29][30][31]. These studies first divided the in-vehicle space into multiple zones (e.g., right mirror, rear-view mirror, and speed meter areas).…”

Section: Head-gaze Estimationmentioning

confidence: 99%

“…Finally, the authors of [23][24][25][26][27] trained a deep learning model and conventional machine learning algorithms using the captured RGB images to estimate the head-gaze direction. In addition, the authors of [28][29][30] collected the depth, infrared images, or 3D point cloud data of a driver, which are not primarily affected by lighting conditions, to estimate the direction of the driver's head-gaze. In particular, [22,31] captured drivers in multiple views (i.e., frontal and side-views) with an RGB or an infrared modality to monitor their attention while driving.…”

Section: Head-gaze Estimationmentioning

confidence: 99%

See 2 more Smart Citations

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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