Flexible and efficient communication in microservices-based stream analytics pipeline

    公开(公告)号:US12047467B2

    公开(公告)日:2024-07-23

    申请号:US18321880

    申请日:2023-05-23

    CPC classification number: H04L67/55 H04L67/563

    Abstract: A pull-based communication method for microservices-based real-time streaming video analytics pipelines is provided. The method includes receiving a plurality of frames from a plurality of cameras, each camera including a camera sidecar, arranging a plurality of detectors in layers such that a first detector layer includes detectors with detector sidecars and detector business logic, and the second detector layer includes detectors with only sidecars, arranging a plurality of extractors in layers such that a first extractor layer includes extractors with extractor sidecars and extractor business logic, and the second extractor layer includes extractors with only sidecars, and enabling a mesh controller, during registration, to selectively assign inputs to one or more of the detector sidecars of the first detector layer and one or more of the extractor sidecars of the first extractor layer to pull data items for processing.

    DYNAMIC RESOURCE MANAGEMENT FOR STREAM ANALYTICS

    公开(公告)号:US20240118938A1

    公开(公告)日:2024-04-11

    申请号:US18474658

    申请日:2023-09-26

    CPC classification number: G06F9/5055

    Abstract: A computer implemented method is provided for resource management of stream analytics at each individual node that includes computing a mean of output processing rate of microservices in a pipeline; and evaluating a state of each microservice of the microservices in the pipeline. The computer implemented method also includes selecting a single microservice from the pipeline for updating resources for an action that changes the state in single the microservice that is selected; and performing resource allocation update for the selected microservice. The computer implemented method may also include updating the state of the selected microservice.

    Time-series based analytics using video streams

    公开(公告)号:US11574461B2

    公开(公告)日:2023-02-07

    申请号:US17197403

    申请日:2021-03-10

    Abstract: Methods and systems for detecting and predicting anomalies include processing frames of a video stream to determine values of a feature corresponding to each frame. A feature time series is generated that corresponds to values of the identified feature over time. A matrix profile is generated that identifies similarities of sub-sequences of the time series to other sub-sequences of the feature time series. An anomaly is detected by determining that a value of the matrix profile exceeds a threshold value. An automatic action is performed responsive to the detected anomaly.

    VIDEO ANALYTIC PROCESSING WITH NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE

    公开(公告)号:US20220114369A1

    公开(公告)日:2022-04-14

    申请号:US17497246

    申请日:2021-10-08

    Abstract: Systems and methods for video analytic processing with neuro-symbolic artificial intelligence are provided. These systems and methods include detecting and extracting one or more objects from one or more video frames, and identifying the attributes associated with each of the one or more objects. These further include extracting context from a question, and compiling a series of inquiries to identify the information needed to answer the question and identify missing information. These further include storing intermediate information about the extracted objects and identified attributes, and determining whether the question requires further modeling of data to obtain missing information. These further include mining the one or more video frames for missing information, and compiling the intermediate information from the data storage and missing information based on the context of the question to produce a final answer.

    VIDEO ANALYTIC SYSTEM FOR CROWD CHARACTERIZATION

    公开(公告)号:US20210303870A1

    公开(公告)日:2021-09-30

    申请号:US17208572

    申请日:2021-03-22

    Abstract: A computer-implemented method for characterizing a crowd that includes recording a video stream of individuals at a location having at least one reference point for viewing; and extracting the individuals from frames of the video streams. The method may further include assigning tracking identification values to the individuals that have been extracted from the video streams; and measuring at least one type classification from the individuals having the tracking identification values. The method may further include generating a crowd designation further characterizing the individuals having the tracking identification values in the location, the crowd designation comprising at least one measurement of probability that the individuals having the tracking identification values in the location view the at least one reference point for viewing.

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