CarSafe demo

You, Chuang-Wen; Montes-de-Oca, Martha; Bao, Thomas J.; Lane, Nicholas D.; Lu, Hong; Cardone, Giuseppe; Torresani, Lorenzo; Campbell, Andrew T.

doi:10.1145/2370216.2370304

Cited by 7 publications

(3 citation statements)

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“…The recently developed software employs images captured by the computer's camera to identify faces. The Viola and Jones algorithm [6] utilizing Haar-based cascade classifiers is leveraged to achieve this objective.…”

Section: Identifying Facial Featuresmentioning

confidence: 99%

“…The existing techniques for detecting drowsiness can be classified into two major categories: intrusive and non-intrusive. Intrusive techniques such as [2] - [4] employ measuring devices that are directly attached to the human body, whereas non-intrusive techniques such as [5] - [6] do not require any physical contact. While intrusive methods have been proposed in earlier studies, they are often impractical since they require additional hardware.…”

Section: Introductionmentioning

confidence: 99%

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Image-based Driver Alert System for Prevention of Fatigue-related Accidents

Reddy,

Krishna,

Sukanya

et al. 2023

E3S Web Conf.

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The objective of this project is to design a driver unconsciousness detection system using image processing to detect drowsiness and unconsciousness in drivers, thereby preventing accidents resulting from driver fatigue. Driver fatigue is a serious road safety issue, with approximately 20% of all road accidents attributed to this cause. Conventional drowsiness detection systems rely on physiological monitoring, which can be unreliable, expensive, and challenging to implement and maintain. In contrast, the proposed system monitors a sequence of images to identify facial and behavioral patterns indicative of drowsiness or unconsciousness. By detecting facial landmark points and analyzing the duration of eye closure, the system can accurately classify the driver’s state and take appropriate measures such as reducing the vehicle’s speed and alerting emergency services of the driver’s geo-location. The successful implementation of this system holds immense potential for substantially reducing the number of accidents resulting from driver fatigue, thereby mitigating the loss of lives and injuries.

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Section: Identifying Facial Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image-based Driver Alert System for Prevention of Fatigue-related Accidents

Reddy,

Krishna,

Sukanya

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Sensing applications like CareSafe [20], BikeNet [21], CenceMe [22], NoiseTube and MobiShop [17] amongst several others use the capability of multiple sensors surrounding the human to send context feedback to mobile users. Efforts to make mobile sensing systems more energy-efficient range from low-power processors [4], sensing pipelines [6], bidirectional feedback pipelines [5] and context correlation with association rules [7].…”

Section: Related Workmentioning

confidence: 99%