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Optical flow and driver's kinematics analysis for state of alert sensing.

Jiménez-Pinto J, Torres-Torriti M - Sensors (Basel) (2013)

Bottom Line: Our experiments show the effectiveness of the approach with a correct eyes detection rate of 99.41%, on average.The results obtained with the proposed approach in an experiment involving fifteen persons under different levels of sleep deprivation also confirm the discriminability of the fatigue levels.In addition to the measurement of fatigue and drowsiness, the pose tracking capability of the proposed approach has potential applications in distraction assessment and alerting of machine operators.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Pontificia Universidad Catòlica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile. jejimenp@puc.cl

ABSTRACT
Road accident statistics from different countries show that a significant number of accidents occur due to driver's fatigue and lack of awareness to traffic conditions. In particular, about 60% of the accidents in which long haul truck and bus drivers are involved are attributed to drowsiness and fatigue. It is thus fundamental to improve non-invasive systems for sensing a driver's state of alert. One of the main challenges to correctly resolve the state of alert is measuring the percentage of eyelid closure over time (PERCLOS), despite the driver's head and body movements. In this paper, we propose a technique that involves optical flow and driver's kinematics analysis to improve the robustness of the driver's alert state measurement under pose changes using a single camera with near-infrared illumination. The proposed approach infers and keeps track of the driver's pose in 3D space in order to ensure that eyes can be located correctly, even after periods of partial occlusion, for example, when the driver stares away from the camera. Our experiments show the effectiveness of the approach with a correct eyes detection rate of 99.41%, on average. The results obtained with the proposed approach in an experiment involving fifteen persons under different levels of sleep deprivation also confirm the discriminability of the fatigue levels. In addition to the measurement of fatigue and drowsiness, the pose tracking capability of the proposed approach has potential applications in distraction assessment and alerting of machine operators.

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Normal distributions of the different states of alert calculated for the awake, semi-drowsy and drowsy subjects, showing the means, the confidence intervals (CI) and the identification threshold between one level of alertness and the other.
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f1-sensors-13-04225: Normal distributions of the different states of alert calculated for the awake, semi-drowsy and drowsy subjects, showing the means, the confidence intervals (CI) and the identification threshold between one level of alertness and the other.

Mentions: The main tasks of the preliminary processing algorithms are the reduction of the image size to 384 × 288 and the execution of the Viola-Jones face detection algorithm when the system initializes to provide for a gross estimate of the head location. Reducing the images allows the system to achieve a frame rate of 16.5 fps using a 2.2 GHz CPU, which is a processing rate sufficient for on-line real-time operation of the system. It is to be noted that for fatigue detection purposes, any vision-based system should have a sampling and processing rate of at least 5–10-times per second, because:(i) eye blinks last between 0.1–0.4 seconds [53]; and (ii) microsleeps last from a fraction of a second up to 30 seconds, but more typically three to 14 seconds, according to the experimental studies in [54]. Furthermore, the resolution of the PERCLOS measurements computed at 16.5 fps is enough to obtain clearly distinguishable states of fatigue, as shown by our results (see, for example, Figure 1).


Optical flow and driver's kinematics analysis for state of alert sensing.

Jiménez-Pinto J, Torres-Torriti M - Sensors (Basel) (2013)

Normal distributions of the different states of alert calculated for the awake, semi-drowsy and drowsy subjects, showing the means, the confidence intervals (CI) and the identification threshold between one level of alertness and the other.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673081&req=5

f1-sensors-13-04225: Normal distributions of the different states of alert calculated for the awake, semi-drowsy and drowsy subjects, showing the means, the confidence intervals (CI) and the identification threshold between one level of alertness and the other.
Mentions: The main tasks of the preliminary processing algorithms are the reduction of the image size to 384 × 288 and the execution of the Viola-Jones face detection algorithm when the system initializes to provide for a gross estimate of the head location. Reducing the images allows the system to achieve a frame rate of 16.5 fps using a 2.2 GHz CPU, which is a processing rate sufficient for on-line real-time operation of the system. It is to be noted that for fatigue detection purposes, any vision-based system should have a sampling and processing rate of at least 5–10-times per second, because:(i) eye blinks last between 0.1–0.4 seconds [53]; and (ii) microsleeps last from a fraction of a second up to 30 seconds, but more typically three to 14 seconds, according to the experimental studies in [54]. Furthermore, the resolution of the PERCLOS measurements computed at 16.5 fps is enough to obtain clearly distinguishable states of fatigue, as shown by our results (see, for example, Figure 1).

Bottom Line: Our experiments show the effectiveness of the approach with a correct eyes detection rate of 99.41%, on average.The results obtained with the proposed approach in an experiment involving fifteen persons under different levels of sleep deprivation also confirm the discriminability of the fatigue levels.In addition to the measurement of fatigue and drowsiness, the pose tracking capability of the proposed approach has potential applications in distraction assessment and alerting of machine operators.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Pontificia Universidad Catòlica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile. jejimenp@puc.cl

ABSTRACT
Road accident statistics from different countries show that a significant number of accidents occur due to driver's fatigue and lack of awareness to traffic conditions. In particular, about 60% of the accidents in which long haul truck and bus drivers are involved are attributed to drowsiness and fatigue. It is thus fundamental to improve non-invasive systems for sensing a driver's state of alert. One of the main challenges to correctly resolve the state of alert is measuring the percentage of eyelid closure over time (PERCLOS), despite the driver's head and body movements. In this paper, we propose a technique that involves optical flow and driver's kinematics analysis to improve the robustness of the driver's alert state measurement under pose changes using a single camera with near-infrared illumination. The proposed approach infers and keeps track of the driver's pose in 3D space in order to ensure that eyes can be located correctly, even after periods of partial occlusion, for example, when the driver stares away from the camera. Our experiments show the effectiveness of the approach with a correct eyes detection rate of 99.41%, on average. The results obtained with the proposed approach in an experiment involving fifteen persons under different levels of sleep deprivation also confirm the discriminability of the fatigue levels. In addition to the measurement of fatigue and drowsiness, the pose tracking capability of the proposed approach has potential applications in distraction assessment and alerting of machine operators.

Show MeSH
Related in: MedlinePlus