A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

Systems and methods are described to dynamically generate metadata for a live media stream. The system determines that a first user on a social media network has started a live media stream. In response, the system identifies a topic of the live media stream based on a frame of the live media stream and identifies another person featured in the frame of the live media stream based on social connections of the first user in the social media network. The system then generates a title for the live media stream based on the identified topic and the identified person, and transmits a notification to a second user that the first user is streaming live, where the notification includes the generated title.

A lighting effect control method includes: obtaining a video frame image sequence by dividing frames of a to-be-tested video stream; sequentially acquiring one of untraversed video frame images in the video frame image sequence as a testing video frame image; according to a recognition result of the testing video frame image, determining that there is at least one target virtual object in the testing video frame image, and determining state information of the target virtual object in the testing video frame image, wherein the recognition result of the testing video frame image is obtained by performing an image recognition process on the testing video frame image; and controlling the lighting effect of a lighting device based on the state information.

A system configured to receive video and/or images from an image capture device over a livestock path, generate feature maps from an image of the video by applying at least a first convolutional neural network, slide a window across the feature maps to obtain a plurality of anchor shapes, determine if each anchor shape contains an object to generate a plurality of regions of interest, each of the plurality of regions of interest being a non-rectangular, polygonal shape, extract feature maps from each region of interest, classify objects in each region of interest, in parallel with classification, predict segmentation masks on at least a subset of the regions of interest in a pixel-to-pixel manner, identify individual animals within the objects based on classifications and the segmentation masks, and count individual animals based on identification, and provide the count to a digital device for display, processing, and/or reporting.

A computer-implemented method, system and computer program product for embedding contextual information in an image or video frames. A generative adversarial network (GAN) is trained to provide contextual information to be embedded in an image or video frames, where the contextual information includes text, sound and/or video frames that provides context to the image or video frames. After training the GAN, an image or video frames are received to be embedded with contextual information if necessary. Features are then extracted from the received image/video frames. An image(s) or video frame(s) are identified in a database using the GAN associated with features with a similarity to the extracted features of the received image/video frames that exceeds a threshold value. Such identified images and/or video frames are associated with “references” containing contextual information which are extracted. The received image/video frames are then augmented with the extracted references to provide context.

A system and method for automated generation of control signals for sexual stimulation devices from usage history and other data. The system and method involve analyzing historical usage and other data for a user for a device or devices, processing the data through machine learning algorithms, and generating new or recombined patterns of stimulation based on the outputs from the machine learning algorithms. The resulting automated control signals represent partially or fully customized stimulation for a given user which evolve over time as the user continues to use the device or devices.

Method and system for wild animal deterrence, the method includes obtaining video data by a monitor camera of a home wildlife deterrence system; classifying, based on the obtained video data, an object in the video data as a particular type of a wild animal; selecting an action to perform based on the particular type of the wild animal that the object is classified as; and triggering the action to be performed. The method also includes determining that the particular type of the wild animal matches a label of a candidate action in a set of candidate actions, wherein each candidate action in the set of candidate actions indicates at least one type of wild animal; and in response to determining that the particular type of the wild animal matches a label of the candidate action in the set of candidate actions, selecting the candidate action as the action to perform.

A video processing apparatus includes a registration component that registers configuration information associated with the video information; a filter component that filters the video frame such that all but the area of interest is excluded in a filtered video frame; a configuration component that configures a plurality of neural networks in at least one of a parallel configuration, sequential configuration, mixed parallel and sequential configuration that provides a configured plurality of neural networks; a processing component that processes the filtered video frame using the configured plurality of neural networks that provides insight information; a display that provides insight information to a user; and a storage component that stores the configuration information and insight information in persistent cloud-based storage. A corresponding method of processing video information and computer-readable medium are also disclosed.

Systems and methods described herein utilize synthetic pixel generation using a custom neural network to generate synthetic versions of objects hidden by occlusions for effective detection and tracking. A computing device stores an object detector model and a synthetic image generator model; receives a video feed; detects objects of interest in a current frame of the video feed; identifies an occluded object in the current frame; retrieves a previous frame from the video feed; generates synthetic data based on the previous frame for the occluded object; and forwards a modified version of the current frame to an object tracking system, wherein the modified version of the current frame includes the synthetic data.

Techniques are disclosed for systems and methods to provide remote sensing imagery for mobile structures. A remote sensing imagery system includes a radar assembly mounted to a mobile structure and a coupled logic device. The radar assembly includes an imaging system coupled to or within the radar assembly and configured to provide image data associated with the radar assembly. The logic device is configured to receive radar returns corresponding to a detected target from the radar assembly and image data corresponding to the radar returns from the imaging system, and then generate radar image data based on the radar returns and the image data. Subsequent user input and/or the sensor data may be used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.