The present disclosure provides an apparatus and method for evaluate the quality of a three dimensional (3D) point cloud. The apparatus comprises an image segmenter to generate a segmented two-dimensional (2D) image for each of the plurality of images; a 2D mask generator to generate a 2D mask for each of the plurality of images from the 3D point cloud; a comparator to compare the segmented 2D image with the 2D mask to obtain a comparison result for each image; and an evaluator to evaluate the quality of the 3D point cloud based on aggregated comparison results for the plurality of images.

An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

Aspects of the disclosure provide for mechanisms for identification of fields in documents using neural networks. A method of the disclosure includes obtaining a layout of a document, the document having a plurality of fields, identifying the document, based on the layout, as belonging to a first type of documents of a plurality of identified types of documents, identifying a plurality of symbol sequences of the document, and processing, by a processing device, the plurality of symbol sequences of the document using a first neural network associated with the first type of documents to determine an association of a first field of the plurality of fields with a first symbol sequence of the plurality of symbol sequences of the document.

Active sensor fusion systems and methods may include a plurality of sensors, a plurality of detection algorithms, and an active sensor fusion algorithm. Based on detection hypotheses received from the plurality of detection algorithms, the active sensor fusion algorithm may instruct or direct modifications to one or more of the plurality of sensors or the plurality of detection algorithms. In this manner, operations of the plurality of sensors or processing of the plurality of detection algorithms may be refined or adjusted to provide improved object detection with greater accuracy, speed, and reliability.

A method for controlling a refrigerator performed by an artificial intelligence computing device may include photographing a food material stored inside of the refrigerator; comparing the photographed image with a preconfigured previous image, and transmitting storage information of the food material to a cloud according to a comparison result; learning the transmitted storage information of the food; determining a stock state of the food material based on the learned storage information of the food material; and determining whether to transmit relation information related to the food material depending on the determined stock state of the food material. One or more of the artificial intelligence computing device according to the present disclosure may be linked with an Artificial Intelligence module, a drone (Unmanned Aerial Vehicle, UAV), a robot, an Augmented Reality (AR) device, a virtual reality (VR) device, a device related to 5G service, and the like.

A vehicle accident detection method and system is provided. The method includes receiving location coordinates associated with a location of an occurring vehicular accident. Data associated with possible causes of the vehicular accident is received from sensors. Traffic related rules associated with a geographical location are retrieved and analyzed with respect to the data. Parameters associated with at least one vehicle involved in the vehicular accident and a possible cause are determined via execution of programming logic and transmitted to additional systems. The possible cause for the vehicular accident is determined from all possible causes based on matching current and historical accident circumstances. Additionally, weighting factors may be available and adjusted over time for accurate accident detection. A possible cause comprising a greatest weighting factor may be used to identify a most likely cause.

A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices. The processing circuit is configured to determine, based on the events, whether a false alarm rule has triggered, where the false alarm rule indicates relationships between one or more of the events that is indicative of a situation at the building site that causes the false alarm, generate a parameter update for at least one of the plurality of building devices in response to determining that the false alarm rule has triggered, and implement the parameter update by providing, via the communications interface, the parameter update to the at least one of the building devices.

An integrated security management system is provided. The system includes an application server and a plurality of sensors deployed in a geographical area. The application server receives first sensor data from the plurality of sensors and provide to a trained classification model as input and detects a security alert based on output thereof. The application server determines a patrol route that encompasses a location of security alert and transmits a surveillance request to electronic device of a security operator to patrol the patrol route and identifies one or more sensors that covers the location of the security alert and receives second sensor data therefrom based on location of the electronic device being same as location of the security alert. The application server further re-trains the classification model based on the second sensor data when feedback received from the electronic device indicates the security alert to be a false positive.

Systems and methods of generating interpretive data associated with data sets. Embodiments of systems may be for adapting Grad-CAM methods for embedding networks. The system includes a processor and a memory. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a subject data set; generate a feature embedding based on the subject data set; determine an embedding gradient weight based on a prior-trained embedding network and the feature embedding associated with the subject data set, the prior-trained embedding network defined based on a plurality of embedding gradient weights respectively corresponding to a feature map generated based on a plurality of training samples, and wherein the embedding gradient weight is determined based on querying a feature space for the feature embedding associated with the subject data set; and generate signals for communicating interpretive data associated with the embedding gradient weight.

Embodiments of the present disclosure relate to systems and methods for reinforcement learning based content recommendation. The method includes receiving configuration data for creation of a reinforcement learning model, generating a plurality of correlation matrices, receiving a request for content for providing to a user, determining a user context, the user context characterizing an aggregation of attributes of the user, and selecting a next piece of content from a database of pieces of content. The method can include presenting the selected piece of content to the user, receiving user inputs in response to the presenting of the selected piece of content to the user, and updating the value characterizing the outcome of previous presentation of the selected piece of content based on the received user input.