Systems and methods for provisioning real-time three-dimensional maps, including: at a first processing stage: updating a three-dimensional map of an environment based on received point cloud data; extracting three-dimensional proposals of objects of the environment; projecting the three-dimensional proposals onto a two-dimensional image of the environment; generating two-dimensional proposals of the objects of the environment; at a second processing stage: detecting the objects of the environment from the two-dimensional image of the environment; generating two-dimensional bounding boxes of the objects of the environment; matching the two-dimensional bounding box with a particular two-dimensional proposal of the two-dimensional proposals; and labeling, for each two-dimensional proposal that is matched to a two-dimensional bounding box, the three-dimensional proposal corresponding to the two-dimensional proposal with semantic information associated with the two-dimensional bounding box that is matched to the two-dimensional proposal to update the three-dimensional map.

Disclosed herein are an object recognition apparatus of an automated driving system using error removal based on object classification and a method using the same. The object recognition method is configured to train a multi-object classification model based on deep learning using training data including a data set corresponding to a noise class, into which a false-positive object is classified, among classes classified by the types of objects, to acquire a point cloud and image data respectively using a LiDAR sensor and a camera provided in an autonomous vehicle, to extract a crop image, corresponding to at least one object recognized based on the point cloud, from the image data and input the same to the multi-object classification model, and to remove a false-positive object classified into the noise class, among the at least one object, by the multi-object classification model.

Disclosed is a method for augmenting a video footage of a surveillance space with a target three-dimensional (3D) object from one or more perspectives for training an artificial intelligence (AI) model, comprising: acquiring the video footage from a target camera in the surveillance space; determining a ground plane and screen coordinates of corners of the ground plane; normalizing screen coordinates from the ground plane and determining a relative position of each object in the ground plane; preparing a model of the target 3D object to be used for training the AI model; iteratively generating a random position and a random rotation for the target 3D object in the ground plane for positioning the target 3D object in front of or behind a distractor object from among the objects in the ground plane; rendering the model of the target 3D object on the ground plane and composing the rendered 3D object and the ground plane with the acquired video footage to generate a composited image; and calculating coordinates of a bounding box that frames the relative position of the target 3D object in the composited image.

A location identification system analyzes information received corresponding to a device detected in a room of a patient. On detecting a location identification of the device, the system assigns the device to the location corresponding to the location identification. In embodiments, the system retrieves patient and care team information for the location. The location and patient and care team information may be communicated to a central video monitoring system.

The present application discloses a high-precision mapping method and device, which relates to the field of autonomous driving. A specific implementation includes: acquiring global initial poses of multiple point clouds, where the point clouds are point clouds of a location for which a map is to be built and are collected by a lidar using a multi-circle collection mode; dividing the multiple point clouds into multiple spatial submap graphs according to a spatial distribution relationship of the multiple point clouds; optimizing, for each spatial submap graph, global initial poses of point clouds belonging to the spatial submap graph to acquire global poses of the point clouds in each spatial submap graph; and stitching the multiple spatial submap graphs together according to global poses of the point clouds in the multiple spatial submap graphs to acquire a base graph of the map to be built.

A wearable apparatus and method are provided for capturing image data. In one implementation, a wearable apparatus for capturing image data is provided. The wearable apparatus includes at least one image sensor for capturing image data of an environment of a user, wherein a field of view of the image sensor includes a chin of the user. The wearable apparatus includes two or more microphones, and an attachment mechanism configured to enable the image sensor and microphones to be worn by the user. The wearable apparatus includes a processing device programmed to capture at least one image, identify the chin of the user to obtain a location of the chin, select a microphone from the two or more microphones based on the location, process input from the selected microphone using a first processing scheme, and process input from a microphone that is not selected using a second processing scheme.

A driving determination device includes an acquirer configured to acquire at least a captured image of a driving body in a driving direction and information that changes with movement of the driving body; a driving level calculator configured to calculate a driving level for evaluating a driving method for the driving body for each predetermined determination item, using at least one of the acquired captured image and the acquired information that changes with the movement of the driving body; an itemized calculator configured to calculate values based on a plurality of the calculated driving levels for each determination item; and an evaluation result calculator configured to calculate a value for comprehensively evaluating the driving method for the driving body, using the values based on the driving levels for each determination item.

Technology disclosed herein may involve a computing system that (i) generates (a) a first feature map based on a first visual input from a first perspective of a scene utilizing at least one first neural network and (b) a second feature map based on a second visual input from a second, different perspective of the scene utilizing at least one second neural network, where the first perspective and the second perspective share a common dimension, (ii) based on the first feature map and a portion of the second feature map corresponding to the common dimension, generates cross-referenced data for the first visual input, (iii) based on the second feature map and a portion of the first feature map corresponding to the common dimension, generates cross-referenced data for the second visual input, and (iv) based on the cross-referenced data, performs object detection on the scene.

Aggregating data and storing information in connection with a digital sticky note that can be displayed in connection with a file. A method includes generating a digital sticky note to be stored in connection with a file. The method includes storing a coordinate location in connection with the digital sticky note, wherein the coordinate location indicates where the digital sticky note should be displayed within the file. The method includes aggregating data to be stored in connection with the digital sticky note, wherein the data comprises information applicable to the coordinate location.

Apparatuses, methods, systems, and program products are disclosed for tracking items. An identification module identifies an item using one or more sensors of an information handling device. A location module receives location data for the item in response to identifying the item. A communication module shares the location data with one or more different information handling devices.