Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model

Choi, In-Ho; Jeong, Chan-Hee; Kim, Yong-Guk

doi:10.3390/app6050137

Cited by 36 publications

(16 citation statements)

References 16 publications

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“…Choi, In-Ho, et al [12] considered both head nodding and eye-blinking for driver's attention label detection. They have used Discriminative Bayesian-Active Shape Model (DB-ASM).…”

Section: B Models That Work For Driver's Distraction Detectionmentioning

confidence: 99%

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Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence

Khan¹,

Debnath²

2020

IJIGSP

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This paper addresses the problem of identifying certain human behavior such as distraction and also predicting the pattern of it. This paper proposes an artificial emotional intelligent or emotional AI algorithm to detect any change in visual attention for individuals. Simply, this algorithm detects human's attentive and distracted periods from video stream. The algorithm uses deviation of normal facial alignment to identify any change in attentive and distractive activities, e.g., looking to a different direction, speaking, yawning, sleeping, attention deficit hyperactivity and so on. For detecting facial deviation we use facial landmarks but, not all landmarks are related to any change in human behavior. This paper proposes an attribute model to identify relevant attributes that best defines human's distraction using necessary facial landmark deviations. Once the change in those attributes is identified, the deviations are evaluated against a threshold based emotional AI model in order to detect any change in the corresponding behavior. These changes are then evaluated using time constraints to detect attention levels. Finally, another threshold model against the attention level is used to recognize inattentiveness. Our proposed algorithm is evaluated using video recording of human classroom learning activity to identify inattentive learners. Experimental results show that this algorithm can successfully identify the change in human attention which can be used as a learner or driver distraction detector. It can also be very useful for human distraction detection, adaptive learning and human computer interaction. This algorithm can also be used for early attention deficit hyperactivity disorder (ADHD) or dyslexia detection among patients.

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“…Choi, In-Ho, et al [12] considered both head nodding and eye-blinking for driver's attention label detection. They have used Discriminative Bayesian-Active Shape Model (DB-ASM).…”

Section: B Models That Work For Driver's Distraction Detectionmentioning

confidence: 99%

“…Both model [11] and [12] work for driver's distraction detection but, none of them consider the case when a driver is talking over phone or not looking straight.…”

Section: B Models That Work For Driver's Distraction Detectionmentioning

confidence: 99%

Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence

Khan¹,

Debnath²

2020

IJIGSP

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“…For example, Mbouna et al [15] developed an approach to extract the visual features from the eyes and head pose of the drivers, and then support vector machines (SVMs) was used to classify the fatigue levels. Choi et al [16] trained the hidden Markov models (HMMs) to model the temporal behaviors of head pose and eye-blinking for identifying whether the driver is drowsy or not. However, these approaches relied on hand-crafted features which have shown limited efficacy in real-time monitoring and can be inaccurate when driver/operator wear the sunglasses or under considerable variation of illumination conditions [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Monitoring and Alerting of Crane Operator Fatigue Using Hybrid Deep Neural Networks in the Prefabricated Products Assembly Process

Li¹,

Chi²,

Zhang³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Crane operators fatigue is one of the significant constraints should be monitored. Otherwise, it may lead to inefficient crane operations and safety issues. Recently, many deep neural networks have been developed for fatigue monitoring of vehicle drivers by processing the image or video data. However, the challenge is to distinguish the slight variations of facial features among still and motion frames (e.g., nodding and head tilt, yawning and talking). It can be exacerbated in the scenarios for crane operators due to their constant head moving to track the loads' position and recurrent communication (talking) with crane banksman. In contrast to previous approaches, which models spatial information and traditional temporal information for sequential processing, this study proposes a hybrid model can not only extract the spatial features by customized convolutional neural networks (CNN) but also enrich the modeling dynamic motions in the temporal dimension through the deep bidirectional long short-term memory (DB-LSTM). This hybrid model is trained and evaluated on the very popular dataset NTHU-DDD, and the results show that the proposed architecture achieves 93.6% overall accuracy and outperform the previous models in the literature.

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“…Eyelid closure detection systems are popular, but they have poor performance under low-light conditions. Furthermore, studies show that many micro-sleeps during driving occur with the driver's eyes open [13]. Some systems rely on head nodding detection using different sensors, such as specialized capacitive sensors [30], and IMU sensors worn on the head [4,39].…”

Section: State Of the Artmentioning

confidence: 99%

MagTrack

Huang

Chen

Lin

2019

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services

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Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver's bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver's postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.

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Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model

Cited by 36 publications

References 16 publications

Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence

Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence

Monitoring and Alerting of Crane Operator Fatigue Using Hybrid Deep Neural Networks in the Prefabricated Products Assembly Process

MagTrack

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