An example method includes causing a light projecting system of a distance sensor to project a three-dimensional pattern of light onto an object, wherein the three-dimensional pattern of light comprises a plurality of points of light that collectively forms the pattern, causing a light receiving system of the distance sensor to acquire an image of the three-dimensional pattern of light projected onto the object, causing the light receiving system to acquire a two-dimensional image of the object, detecting a feature point in the two-dimensional image of the object, identifying an interpolation area for the feature point, and computing three-dimensional coordinates for the feature point by interpolating using three-dimensional coordinates of two points of the plurality of points that are within the interpolation area.

Aspects of the present disclosure are directed to creating and administering artificial reality collaborative working environments and providing interaction modes for them. An XR work system can provide and control such artificial reality collaborative working environments to enable, for example, A) links between real-world surfaces and XR surfaces; B) links between multiple real-world areas to XR areas with dedicated functionality; C) maintaining access, while inside the artificial reality working environment, to real-world work tools such as the user's computer screen and keyboard; D) various hand and controller modes for different interaction and collaboration modalities; E) use-based, multi-desk collaborative room configurations; and F) context-based auto population of users and content items into the artificial reality working environment.

A handwash monitoring system includes: an imaging device; and a processor. The processor detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging device before handwashing, and detects a second candidate abnormality existing in the hand of the user from a second image captured by the imaging device after the handwashing. The processor determines a type of an abnormality on the hand of the user based on a difference between a shape of the first candidate abnormality and a shape of the second candidate abnormality wherein the first candidate abnormality and the second candidate abnormality are detected from an identical region.

Driver attention and hand placement systems and methods are disclosed herein. An example method includes providing warning messages to a driver of a vehicle based on steering wheel input or hand-wheel contact by the driver. The warning messages are provided according to a first scheme when the steering wheel input is above a threshold value and according to a second scheme when the steering wheel input is below a threshold value and images obtained by a camera in the vehicle indicate that at least one hand of the driver is on the steering wheel.

A system and method for inferring operator intent by detecting operator focus incorporates cameras positioned within a cockpit or control space of a vehicle and oriented at an operator of the vehicle. The cameras capture images of the operator in a control seat; the images are analyzed (either individually or sequentially) to determine a gaze and/or body pose of the operator (including, e.g., a position and orientation of the torso and limbs). By comparing the determined gaze and/or body pose to the positions and orientations of potential focus targets within the control space (e.g., windows, display units, and/or control panels that the operator may engage with visually and/or physically), the system predicts the most likely future focus target or targets: what the operator is most likely to visually and/or physically engage with next. Operator intent may be further analyzed to identify potentially abnormal or anomalous behavior.

The present invention relates to a device and a control method therefor and, more specifically, the device comprises: a memory for storing at least one command; a depth camera for capturing at least one hand of a user; a display module; and a controller for controlling the memory, the depth camera, and the display module. The controller controls the depth camera so as to capture the at least one hand of a user and controls the display module so as to output a visual feedback that changes on the basis of the captured hand of a user.

Various client devices include displays and one or more image capture devices configured to capture video data. Different users of an online system may authorize client devices to exchange information captured by their respective image capture devices. Additionally, a client device modifies captured video data based on users identified in the video data. For example, the client device changes parameters of the image capture device to more prominently display a user identified in the video data and may further change parameters of the image capture device based on gestures or movement of the user identified in the video data. The client device may apply multiple models to captured video data to modify the captured video data or subsequent capturing of video data by the image capture device.

A feature learning system (100) includes a similarity definition unit (101), a learning data generation unit (102), and a learning unit (103). The similarity definition unit (101) defines a degree of similarity between two classes related to two feature vectors, respectively. The learning data generation unit (102) acquires the degree of similarity, based on a combination of classes to which a plurality of feature vectors acquired as processing targets belong, respectively, and generates learning data including the plurality of feature vectors and the degree of similarity. The learning unit (103) performs machine learning using the learning data.

Multiple cameras capture videos within a secure room. When individuals are detected as entering the room, identities of the individuals are resolved. When an asset is exposed in a field of view of one of the cameras, the individuals' eye and head movements are tracked from the videos with respect to one another and the asset. Additionally, touches made by any of the individuals on the asset are tracked from the videos. The eye and head movements are correlated with the touches or lack of touches according to a security policy for the asset. Any violations of the security policy are written to a secure audit log for the room and the asset.

Disclosed are method and apparatus to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.