Abstract: A method for determining a state of vigilance of a driver in a vehicle using a predetermined image-analyzing algorithm. The method especially includes a step of executing the predetermined algorithm on the generated sequence of images in order to detect a series comprising at least one movement of the head of the driver, a step of determining the speed and/or the amplitude of each identified movement and of the dynamic and static periods of the head of the driver, a step of detecting dynamic and static periods of the head of the driver and of measuring the frequency and duration of each period, and a step of determining a state of vigilance of the driver from the speed and/or the amplitude determined for each identified movement and from the frequency and duration of each detected period.

Abstract: A method for determining a state of vigilance of a driver in a vehicle using a predetermined image-analyzing algorithm. The method especially includes a step of executing the predetermined algorithm on the generated sequence of images in order to detect a series comprising at least one movement of the head of the driver, a step of determining the speed and/or the amplitude of each identified movement and of the dynamic and static periods of the head of the driver, a step of detecting dynamic and static periods of the head of the driver and of measuring the frequency and duration of each period, and a step of determining a state of vigilance of the driver from the speed and/or the amplitude determined for each identified movement and from the frequency and duration of each detected period.

Abstract: A method for determining a level of drowsiness of a driver of a motor vehicle on the basis of a predetermined image analysis algorithm, the vehicle including a camera and a computer, the computer implementing the predetermined algorithm on the basis of a set comprising at least one parameter relating to the attitude of the driver, the method, implemented by the computer, including a phase of learning and a phase of monitoring the state of the driver.

Abstract: To improve the performance for determining obstruction of a stereoscopic system using two cameras or more, a hybrid of local and semi-global methods is provided. For each stereoscopic image formed from simultaneous left and right images, a breakdown of each left and right image into corresponding sectors is applied. Obstruction level is determined by a disparity map by sector, based on left or right images, and in which a disparity is assigned to each pixel corresponding to the best matching score. A determination of density by sector of the disparity map is carried out by reference to a fraction of pixels with a disparity considered to be valid. A state of obstruction of at least one camera is determined based on a weighted average of the probabilities of obscuring of the sectors of the disparity map obtained by comparison between the density of the sectors and a predefined density level.

Abstract: To improve the performance for determining obstruction of a stereoscopic system using two cameras or more, a hybrid of local and semi-global methods is provided. For each stereoscopic image formed from simultaneous left and right images, a breakdown of each left and right image into corresponding sectors is applied. Obstruction level is determined by a disparity map by sector, based on left or right images, and in which a disparity is assigned to each pixel corresponding to the best matching score. A determination of density by sector of the disparity map is carried out by reference to a fraction of pixels with a disparity considered to be valid. A state of obstruction of at least one camera is determined based on a weighted average of the probabilities of obscuring of the sectors of the disparity map obtained by comparison between the density of the sectors and a predefined density level.

Abstract: Method of evaluating the state of alertness of a vehicle driver based on the analysis of the eyelid movements of the driver. A classification of blink durations composed of m classes defined mathematically, and a classification of states of alertness composed of n alertness state classes including a class corresponding to an “alert” state and a class corresponding to a “sleepy” state, delimited by given thresholds of numbers of medium and long duration blinks, are set up. During an evaluation, a duration vector is associated with each blink, of which each component represents the degree of membership of the blink in one of the predefined duration classes, temporal analysis windows are defined at the end of each of which a cumulative duration vector is calculated of which each component consists of the sum ?M,?L of same row components of duration vectors.