Automatic Calibration Method for Driver's Head Orientation in Natural Driving Environment

Fu, Xianping; Guan, Xiao Wei; Peli, Eli; Liu, Hongbo; Luo, Gang

doi:10.1109/tits.2012.2217377

Cited by 56 publications

(23 citation statements)

References 36 publications

(47 reference statements)

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“…In this paper, gaze zones are similar to but less than our previous work [3]. Nine different gaze zones are the two side mirrors, the rear-view mirror, the dashboard, the console, the co-driver glove box and left three zones on the windshield, as shown in Figure 2.…”

Section: Gaze Zonesmentioning

confidence: 75%

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A 3D Driver Head Pose Estimation Method Based on Depth Image

Zhang

2014

Proceedings of International Conference on Internet Multimedia Computing and Service

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The driver's head pose is an indicator of driver's attention and plays a significantly role in driving safety. Traditional 2D color image processing method has disadvantage due to its vulnerability under poor environmental lighting conditions in vehicle. In this paper, we propose a novel method with calibrated gaze zone to estimate the driver head pose combining RGB and depth data. The head rotation can be calculated by Iterative Closest Point algorithm in 3D point cloud. In order to reduce the amount of calculation of ICP for head pose estimation, particle filter is used to track and learn the status of head movement. The head templates of different gaze zones and their neighbors for ICP also make contribution to an accurate result. The experimental results show that our approach has a low error rate and is acceptable in real driving environment.

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Section: Gaze Zonesmentioning

confidence: 75%

“…For the real driving conditions, we improve the gaze zone partitioned method in our prior work [3]. Different gaze zones correspond to different head poses.…”

Section: Introductionmentioning

confidence: 99%

A 3D Driver Head Pose Estimation Method Based on Depth Image

Zhang

2014

Proceedings of International Conference on Internet Multimedia Computing and Service

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“…The authors trained gaze classifiers in a supervised framework to determine 18 gaze zones. Fu et al designed a system that categorizes the head pose into 12 different gaze zones based on facial features [23]. The system automatically learns the zones based on different calibration points, such as side mirrors, rearview mirrors, etc.…”

Section: Related Researchmentioning

confidence: 99%

Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

Tawari

Martin

Trivedi

2014

IEEE Trans. Intell. Transport. Syst.

104

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Abstract-Analysis of a driver's head behavior is an integral part of a driver monitoring system. In particular, the head pose and dynamics are strong indicators of a driver's focus of attention. Many existing state-of-the-art head dynamic analyzers are, however, limited to single-camera perspectives, which are susceptible to occlusion of facial features from spatially large head movements away from the frontal pose. Nonfrontal glances away from the road ahead, however, are of special interest since interesting events, which are critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for head movement analysis, with emphasis on the ability to robustly and continuously operate even during large head movements. The proposed system tracks facial features and analyzes their geometric configuration to estimate the head pose using a 3-D model. We present two such solutions that additionally exploit the constraints that are present in a driving context and video data to improve tracking accuracy and computation time. Furthermore, we conduct a thorough comparative study with different camera configurations. For experimental evaluations, we collected a novel head pose data set from naturalistic on-road driving in urban streets and freeways, with particular emphasis on events inducing spatially large head movements (e.g., merge and lane change). Our analyses show promising results.

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“…The first category is not suitable for trains because they use a track [23]. The second category analyses the changes of driver behaviour, such as eye tracking, yawning, percent eye closure (PERCLOS), blink frequency, nodding frequency, facial position, and inclination of the driver’s head [24,25,26,27,28]. Recent progress in machine vision research and advances in computer hardware technologies have made it possible to measure eyelid movement, face expression and head pose using video cameras.…”

Section: Introductionmentioning

confidence: 99%

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

Zhang

Liu

et al. 2017

Sensors

122

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The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

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Automatic Calibration Method for Driver's Head Orientation in Natural Driving Environment

Cited by 56 publications

References 36 publications

A 3D Driver Head Pose Estimation Method Based on Depth Image

A 3D Driver Head Pose Estimation Method Based on Depth Image

Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

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