An augmented reality method using a mobile device having a display screen and a camera, the method includes receiving an image of a physical object from the camera, the physical object incorporating a machine-readable code, detecting the machine-readable code in the image of the physical object, decoding the machine-readable code, determining a website address of a website in response to decoding the machine-readable code, automatically navigating to the website according to the website address, and automatically receiving a video stream from the website and playing the video on the display screen of the mobile device superimposed over the image of the physical object.

A method and an electronic apparatus for acquiring a floor map of a room layout are provided. The method includes acquiring a depth map and a two-dimensional (2D) image of an interior of a room; identifying a boundary line between a ceiling and a wall of the room in the acquired 2D image; determining valid pixels among pixels on the boundary line according to a first preset rule; identifying an electronic device in the room and determining a location of the electronic device; acquiring the floor map of the room layout by projecting three-dimensional (3D) coordinates of actual points corresponding to the valid pixels and 3D coordinates of the determined location of the electronic device onto a horizontal plane, wherein, the floor map of the room layout comprises connection lines between projections of the 3D coordinates of the actual points corresponding to the valid pixels and an icon indicating a type and the location of the electronic device, and wherein, the 3D coordinates of the actual points corresponding to the valid pixels are determined based on the depth map and the 2D image.

Disclosed is a data augmentation-based space analysis model learning apparatus including one or more processors, wherein the operation performed by the processor includes acquiring a plurality of space images and labeling a class specifying space information corresponding to each of the plurality of space images or acquiring the plurality of space images to which the class is labeled and generating learning data, generating a second space image by changing some or all of pixel information included in a first space image among the plurality of space images and augmenting the learning data, labeling a class labeled to the first space image, to the second space image, and learning a weight of a model designed based on a predetermined image classification algorithm, for deriving a correlation between a space image included in the learning data and a class labeled to each of the space images.

Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.

Disclosed are various user-presence/absence detection techniques based on deep learning. These user-presence/absence detection techniques can include building/training a deep-learning model including a user-presence/absence classifier based on training images of a user-seating area of a surgeon console under various clinically-relevant conditions. The trained user-presence/absence classifier can then be used during teleoperation/surgical procedures to monitor/track users in the user-seating area of the surgeon console, and continuously classify captured real-time video images of the user-seating area into either a user-presence classification or a user-absence classification. In some embodiments, the disclosed techniques can be used to detect a user-switching event at the surgeon console when a second user is detected to have entered the user-seating area after a first user is detected to have exited the user-seating area. If the second user is identified as a new user, the disclosed techniques can trigger a recalibration procedure to recalibrate surgeon-console settings for the new user.

The present disclosure describes a method for occupancy class prediction, such as for occupancy class detection in a vehicle. In aspects, the method includes determining, for a plurality of points of time, measurement data related to an area and determining, for a plurality of points of time, occlusion values based on the measurement data. The method further includes selecting, for a present point of time, one of a plurality of modes for occupancy class prediction based on the occlusion values for at least one of the present point of time and a previous point of time and/or based on one of the plurality of modes for occupancy class prediction selected for the previous point of time. The method additionally includes determining, for the present point of time, one of a plurality of predetermined occupancy classes of the area based on the selected mode for the present point of time.

A multi-robot system includes a first a mobile cleaning robot that has a local storage device to store a persistent map of an environment, at least one sensor to sense the environment, and a control module. The control module is configured to: control the mobile cleaning robot to navigate in the environment using the persistent map and sensing data provided by the at least one sensor, share the persistent map with a second mobile cleaning robot, and coordinate with the second mobile cleaning robot to perform cleaning tasks.

Systems and methods for creating a digital model of an indoor space and virtually furnishing the digital model are described. A data collection device photographs or scans an indoor space and produces dimensional data using collected data. The system further includes a pre-processing process, a planes process, a move to origin process, and a segmentation process that analyze and process the dimensional data to create a digital model of the indoor space. The digital model is saved to a database and is viewable on a display and includes a visual scale that corresponds to the spatial dimensions of the indoor space. The system also detects objects present in the indoor space for removal from the digital model. The system also allows creation and placement of visual object representations for display in the digital model.

An automatic measuring system containing configurable integrated circuits is able to process information via captured images. The automatic measuring system includes a metering instrument, a camera, a recognition module, and a localization module. The metering instrument has at least one display for visually displaying a number and measures the amount of measurable substance or resources (i.e., electricity and water) consumed. The camera captures an image of the number representing at least a portion the amount of measurable substance. The recognition module is operable to generate a value in response to the image and the coordinates wherein the coordinates are used to decode the image via restoring captured image to the original readout counter value. The localization module is removably or remotely coupled to the camera and operable to generate the coordinates in accordance with the image captured by the camera.

A method for controlling a smart device is provided. The method comprises identifying a user-furniture interaction activity of a user interacting with a home furnishing product by analyzing sensor data, captured by an imaging sensor, depicting a scene of the user interacting with the home furnishing product. The method further comprises comparing the user-furniture interaction activity against a set of predetermined user-furniture interaction activities, thereby determining a specific predetermined user-furniture interaction activity among the set of predetermined user-furniture interaction activities, wherein each of the predetermined user-furniture interaction activities is associated with a rule of controlling a smart device. The method further comprises controlling the smart device in accordance with the rule.