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In this work an online method for camera-based heart rate detection, also known as Photoplethysmography Imaging (PPGI), is presented. The pulse related signal is obtained from RGB videos of the human face, using an off the shelf camera under ambient light conditions. The algorithm for heart rate estimation is based on the beat-to-beat analysis of the PPGI signal, allowing the estimation of psychophysiological...
Fast expansion of Advanced Driver Assistance Systems (ADAS) market and applications has resulted in a high demand for various accompanying algorithms. In this paper we present an implementation of Driver monitoring algorithm. Main goal of the algorithm is to automatically asses if driver is tired and in that case, raise a proper alert. It is widely used as a standard component of rest recommendation...
Drowsiness is a major cause of accidents, in particular in road transportation. It is thus crucial to develop robust drowsiness monitoring systems. There is a widespread agreement that the best way to monitor drowsiness is by closely monitoring symptoms of drowsiness that are directly linked to the physiology of an operator such as a driver. The best systems are completely transparent to the operator...
This paper addresses two issues for mitigating driver distraction/inattention by using novel video analysis techniques: (a) inside an ego vehicle, driver inattention is monitored through first tracking drivers face/eye region using Riemannian manifold-based particle filters, followed by recognition of dynamic eye states using PPCA (probabilistic principal component analysis) and SVM (support vector...
Drowsiness detection is vital in preventing accidents. Eye state analysis — Detecting whether the eye is open or closed — is critical step for drowsiness detection. In this article we proposed an innovative algorithm for detecting eyes in drowsiness detection which is based on morphology. Morphological processes are one of the powerful instruments for analysing images even with low quality, so it...
Driver drowsiness is a major factor in most driving accidents. In this paper we present a robust and intelligent scheme for driver drowsiness detection employing the fusion of eye closure and yawning detection methods. In this approach, the driver's facial appearance is captured via a camera installed in the car. In the first step, the face region is detected and tracked in the captured video sequence...
In this demo we will present a vision-based smart environment using in-car cameras that can be used for real time tracking and monitoring of a driver in order to detect the driver's drowsiness based on yawning detection. As driver fatigue and drowsiness is a major cause behind a large number of road accidents, the assistive systems that monitor a driver's level of drowsiness and alert the driver in...
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