A light emitting unit emits infrared rays. An imaging element converts incident infrared rays into an electric signal and outputs the electric signal. A control unit estimates a light emission timing at which infrared rays are emitted from another infrared imaging device based on an infrared picture generated based on the electrical signal output from the imaging element, and performs control to cause the light emitting unit to emit infrared rays in a period in which the infrared rays are not emitted from the another infrared imaging device.

A virtual visor system is disclosed that includes a visor having a plurality of independently operable pixels that are selectively operated with a variable opacity. A camera captures images of the face of a driver or other passenger and, based on the captured images, a controller operates the visor to automatically and selectively darken a limited portion thereof to block the sun or other illumination source from striking the eyes of the driver, while leaving the remainder of the visor transparent. The visor system advantageously predicts future positions of the head or eyes of the driver when the driver’s head is in motion. Based on the predictions, the optical state of the visor is updated proactively to anticipate future movements of head of the driver. In this way, some of the negative effects of measurement and processing latencies are mitigated when responding to rapid head motions.

A virtual visor system is disclosed that includes a visor having a plurality of independently operable pixels that are selectively operated with a variable opacity. A camera captures images of the face of a driver or other passenger and, based on the captured images, a controller operates the visor to automatically and selectively darken a limited portion thereof to block the sun or other illumination source from striking the eyes of the driver, while leaving the remainder of the visor transparent. The visor system advantageously detects certain combinations of facial expression and head gestures from which an error or issue with the operation of the visor system can be inferred. In response to such designated combinations of head gestures and facial expressions, the visor system adapts one or more operating or calibration parameters of the visor system to provide more accurate updates to the optical state of the visor.

Performing object localization inside a cabin of a vehicle is provided. A camera image is received of a cabin of a vehicle by an image sensor at a first location with respect to a plurality of seating zones of a vehicle. Object detection on the camera image is performed to identify one or more objects in the camera image. A machine-learning model trained on images taken at the first location is utilized to place the one or more objects into the seating zones of the vehicle according to a plurality of bounding boxes corresponding to the plurality of seating zones for the first location.

A vehicle and control method include: a seat; an image acquirer to acquire an image of the seat; a first type of roof airbag module in a fixed position in a first area of a headlining; a rail member in a second area of the headlining in a left-right direction of a vehicle body; a second type of roof airbag module in the rail member and movable in left and right directions along the rail member; an angle detector to detect a rotation angle of the seat; and a controller. The controller identifies the seat rotation angle based on the image of the seat and controls activation of at least the first type of roof airbag module or the second type of roof airbag module based on at least the seat rotation angle based on the image of the seat or detected by the angle detector.

An electronic device 10 includes an image-capturing unit 11, a line-of-sight detector 12, and a controller 14. The image-capturing unit 11 generates an image corresponding to the view by performing image capturing. The line-of-sight detector detects a line of sight of a subject with respect to the view. The controller 14 functions as a first estimator 15. The first estimator 15 is capable of estimating a first heat map based on the image. The controller 14 calculates the alertness level of the subject based on the first heat map and the line of sight of the subject. The first estimator 15 is constructing using learning data obtained by machine learning the relationship between a learning image and a line of sight of a training subject when an alertness level of the training subject is in a first range.

A virtual visor system is disclosed that includes a visor having a plurality of independently operable pixels that are selectively operated with a variable opacity. A camera captures images of the face of a driver or other passenger and, based on the captured images, a controller operates the visor to automatically and selectively darken a limited portion thereof to block the sun or other illumination source from striking the eyes of the driver, while leaving the remainder of the visor transparent. The virtual visor system advantageously updates the optical state with blocker patterns that including padding in excess of what is strictly necessary to block the sunlight. This padding advantageously provides robustness against errors, allows for a more relaxed response time, and minimizes frequent small changes to the position of the blocker in the optical state of the visor.

An image is received from a camera built into a cabin of a vehicle. The image is demosaiced and its noise is reduced. A segmentation algorithm is applied to the image. A global illumination for the image is solved. Based on the segmentation of the image and the global illumination, a bidirectional reflectance distribution function (BRDF) for color and/or reflectance information of material in the cabin area of the vehicle is solved for. A white balance matrix and a color correction matrix for the image are computed based on the BRDF. The white balance matrix and the color correction matrix are applied to the image, which is then displayed or stored for addition image processing.

A virtual visor system is disclosed that includes a visor having a plurality of independently operable pixels that are selectively operated with a variable opacity. A camera captures images of the face of a driver or other passenger and, based on the captured images, a controller operates the visor to automatically and selectively darken a limited portion thereof to block the sun or other illumination source from striking the eyes of the driver, while leaving the remainder of the visor transparent. The virtual visor system advantageously detects whether the driver of other passenger is performing particular head gestures and updates the optical state of the visor using suitable modified procedures that accommodate the intent or goals of the driver or other passenger that are inferred from the predefined head gesture. In general, the modified procedures reduce distracting or frustrating updates to the optical state of the visor.

A facial structure estimating device 10 includes an acquiring unit 11 and a controller 13. The acquiring unit 11 acquires a facial image. The controller 13 functions as an estimator 16 that estimates a facial structure from a facial image. The controller 13 tracks a starting feature point constituting a facial structure using a tracking algorithm in a facial image of a frame subsequent to a facial image used to estimate the facial structure. The controller 13 obtains a resulting feature point by tracking a tracked feature point using an algorithm in an original frame facial image. The controller 13 selects a learning facial image for which the interval between resulting and starting feature points is less than or equal to a threshold. The controller 13 trains the estimator using the facial image selected for learning and the facial structure estimated by the estimator 16 based on the facial image.