Technical solutions for video event recognition relate to the fields of knowledge graphs, deep learning and computer vision. A video event graph is constructed, and each event in the video event graph includes: M argument roles of the event and respective arguments of the argument roles, with M being a positive integer greater than one. For a to-be-recognized video, respective arguments of the M argument roles of a to-be-recognized event corresponding to the video are acquired. According to the arguments acquired, an event is selected from the video event graph as a recognized event corresponding to the video.

Normalized resolutions are determined for first and second regions of interest of an initial video stream captured by a video capture device located within a physical space. The first region of interest is associated with a first conference participant within the physical space and the second region of interest is associated with a second conference participant within the physical space. Instructions are transmitted to the video capture device to cause the video capture device to capture, at the normalized resolutions, a first video stream associated with the first region of interest and a second video stream associated with the second region of interest. The first and second video streams conform sizes and quality levels of the first and second conference participants within separate user interface tiles of a conferencing software user interface to which the first and second video streams are output.

A system for classifying tasks based on workflow patterns detected on workflows through a real time video feed that shows steps being performed to accomplish a plurality of tasks. Each task is associated with a different set of steps. The system accesses a first set of steps known to be performed to accomplish a first task on the webpages. The first set of steps is represented by a first set of metadata. The system extracts a second set of metadata from the video feed. The second set of metadata represents a second set of steps to perform a second task. The system determines whether the second set of metadata corresponds to the first set of metadata. If it is determined that the second set of metadata corresponds to the first set of metadata, the system classifies the second task in a class to which the first task belongs.

System and method are provided to detect objects in a scene frame of two-dimensional (2D) video using image processing and determine object image coordinates of the detected objects in the scene frame. The system and method deploy a virtual camera in a three-dimensional (3D) environment to create a virtual image frame in the environment and generate a floor in the environment in a plane below the virtual camera. The system and method adjust the virtual camera to change a height and angle relative to the virtual image frame. The system and method generate at an extended reality (XR) coordinate location relative to the floor for placing the detected object in the environment. The XR coordinate location is a point of intersection of a ray cast of the virtual camera through the virtual frame on the floor that translates to the image coordinate in the virtual image frame.

A system for identifying autonomous vehicles includes at least one sensor that may be configured to provide sensor data associated with at least two vehicles. A pre-processing module may be coupled to the at least one sensor and may be configured to determine a set of data including at least car following data based on the sensor data. An autonomous vehicle (AV)/human-driven vehicle (HV) identification neural network may be coupled to the pre-processing module and configured to generate an AV/HV identifier for at least one of the at least two vehicles based on at least the car following data during a predetermined time period.

A method for intersection assistance, the method may include obtaining sensed information regarding an environment of the vehicle; determining an occurrence of an intersection related situation, based on the sensed information; generating one or more intersection driving related decisions; wherein the generating comprises processing, by one or more narrow AI agents of a group of narrow AI agents, at least one out of (a) at least a first part of the sensed information, and (b) an outcome of a pre-processing of at least a second part of the sensed information; and responding to the one or more intersection driving related decisions; wherein the responding comprises at least one out of (a) executing the one or more intersection driving related decisions, and (b) suggesting executing the one or more intersection driving related decisions.

The present disclosure provides a system for monitoring users/workplaces in real-time. The system includes video monitoring device(s) for capturing video frames of a user/workplace. The system also includes a client monitoring module including: a transceiving module for receiving credentials from the user; a user registration and authentication module for authenticating the user; and a processing module for: analysing the video frames in real-time to determine if object of interest(s) is present and accordingly determining at least one action of interest of the at least one object of interest; and determining if the object of interest is passing a predefined threshold. When object of interest passes the predefined threshold frames, the client monitoring module may analyse the video frames and take a preventive action or may send the video frames to a server for further analysis and preventive action.

Aspects relate to systems and methods for parsing and correlating solicitation video content. An exemplary system includes a computing device configured to receive a solicitation video related to a subject, where the solicitation video includes at least an image component and at least an audio component, where the audio component includes audible verbal content related to at least an attribute of the subject, transcribe at least a keyword as a function of the audio component, and associate the subject with at least a job description as a function of the at least a keyword.

Computer and mobile device-based systems and computer-implemented methods are described for automated medication adherence improvement for patients in medication-assisted treatments. The computer and mobile device-based systems includes modules and components to help patients in identifying prescribed medications, logging medication events, and to provide patients with personalized and targeted adherence enhancing interventions consisting of short questions, tips, advices, suggestions, strategies etc. by applying data mining and statistical analysis techniques on the individual and population-level data collected primarily from the same system.

A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.