Embodiments of the invention are directed to a method for providing online test cheating detection. A non-limiting example of the method includes acquiring a test taker's normal test behavior, also referred to as habit data, through a pre-test. During the main test, the habit data is fed as one of inputs to the deep neural network (DNN) along with other real time inputs that represents eye gaze direction and movements of other body parts such as the head, shoulder, and upper body. These real-time input data are extracted from the visual data captured by the test taker's camera. The deep neural network (DNN) is pre-trained using a pre-existing database to distinguish abnormal or suspicious behavior from normal test behavior.

A recognition network is trained for a selected video frame at a desired highest precision using back-propagation and a policy network is trained using back-propagation from the trained recognition network. The recognition network is trained at a lower precision specified by a policy recommended for the selected video frame by the trained policy network. A frame of a given video is inputted to the trained policy network for determination of a precision policy for processing the frame. Video inferencing is performed utilizing the trained policy network and the trained recognition network based on the precision policy.

Exemplary embodiments of the present disclosure are directed towards system and method for generating scores and assigning quality index to videos on digital platform, comprising computing device that comprises video uploading module configured to allow user to record and upload videos on computing device, thereby transferring user uploaded videos to server over network. Server comprising video evaluating module configured to receive user uploaded videos and identifying video frames, thereby identifying different criteria. Video evaluating module configured to evaluate different criteria assigning scores to video frames and computing plurality of metrics of video frames based on assigned scores, then calculates mean and median values of metrics and assign mean and median values of video frame vectors, and combine video frame vectors of each video frame to obtain final video vector. Video evaluating module configured to assign weight to each value of final video vector to identify video quality index.

The embodiments herein disclose a system and a method for providing an online web-based interactive audio-visual platform for note creation, presentation, sharing, organizing, and analysis. The system provides a conceptual and interactive interface to content; analyses a student's notes and instantly determines the accuracy of the conceptual connections made and a student's understanding of a topic. The system enables the student to add and use audio, visual, drawing, text notes, and mathematical equations in addition to those suggested by the note taking solution; to collate notes from various sources in a meaningful manner by grouping concepts using colors, images, and text; and to personalize other maps developed within the same environment while maintaining links back to the original source from which the notes are derived. The system highlights keywords in conjunction with spoken text to complement the advantages of using visual maps to improve learning outcomes.

Exemplary embodiments of present disclosure are directed towards a system and method for extracting objects from videos in real-time to create virtual situations, comprising a computing device comprises video creating and editing module configured to enable a user to record videos and select frames automatically from the user recorded videos thereby transferring the automatically selected frames from the computing device to a server. The server comprises video processing module configured receive the automatically selected frames thereby detecting and extracting objects from the automatically selected frames and transfer extracted objects to computing device and display the extracted objects to the user. The video creating and editing module configured to place the extracted objects on a new frame automatically and allow the user to reposition extracted objects on new frame and enable the user to customize the background and foreground elements in the new frame to create virtual situations.

A computing device accesses video data displaying one or more traffic entities and generates a plurality of sequences from the video data. For each sequence, the computing device identifies a plurality of stimuli in the sequence and applies a machine learning model to generate an output describing the traffic entity. The computing device generates a data structure for storing, for each sequence, information describing the sequence and linking frame indexes of stimuli from the sequence to outputs of the machine learning model. The computing device stores the data structure in association with the video data. Responsive to receiving a selection of a sequence, the computing device loads video data for the sequence. Responsive to receiving a selection of a traffic entity within the video data, the computing device generates a graphical display element including the machine learning model output for the selected traffic entity.

An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system is configured to receive a first frame from the sensor and to identify a first pixel location and a second pixel location within the first frame. The system is further configured to determine (x,y) coordinates by applying a homography to the first pixel location and the second pixel location. The system is further configured to determine an estimated distance between the (x,y) coordinates, to determine an actual distance, and to determine a distance difference between the estimated distance and the actual distance. The system is further configured to compare the distance difference to a difference threshold level and to recompute the homography in response to determining that the distance difference exceeds the difference threshold level.

This application relates to an image display device and method. In one aspect, the image display device includes a communication interface, a user interface, a memory and a processor. The processor may receive, from a first terminal through the communication interface, a stream including a plurality of images captured by the first terminal. The processor may also determine whether the received stream includes a first image in which no face is detected among the plurality of images. The processor may further, in response to determining that the received stream includes the first image, perform image processing on the first image to generate a second image. The processor may further display, through the user interface, the plurality of images by replacing the first image with the second image.

Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for real-time deployment of machine learning networks. One of the operations is performed by the system receiving video data from a video image capturing device. The received video data is converted into multiple video frames. These video frames are encoded into a particular color space format. The system renders a first display output depicting imagery from the multiple encoded video frames. The system performs an inference on the video frames using a machine learning network in order to determine the occurrence of one or more objects in the video frames. The system renders a second display output depicting graphical information corresponding to the determined one or more objects from the multiple encoded video frames. The system then generates a composite display output including the imagery of the first display output overlaid with the graphical information of the second display output.