Self-optimizing video analytics pipelines

    公开(公告)号:US12001513B2

    公开(公告)日:2024-06-04

    申请号:US17522226

    申请日:2021-11-09

    CPC classification number: G06F18/217 G06F9/5027 G06N3/08 G06V10/94 G06V20/46

    Abstract: A method for implementing a self-optimized video analytics pipeline is presented. The method includes decoding video files into a sequence of frames, extracting features of objects from one or more frames of the sequence of frames of the video files, employing an adaptive resource allocation component based on reinforcement learning (RL) to dynamically balance resource usage of different microservices included in the video analytics pipeline, employing an adaptive microservice parameter tuning component to balance accuracy and performance of a microservice of the different microservices, applying a graph-based filter to minimize redundant computations across the one or more frames of the sequence of frames, and applying a deep-learning-based filter to remove unnecessary computations resulting from mismatches between the different microservices in the video analytics pipeline.

    FLEXIBLE AND EFFICIENT COMMUNICATION IN MICROSERVICES-BASED STREAM ANALYTICS PIPELINE

    公开(公告)号:US20230403340A1

    公开(公告)日:2023-12-14

    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.

    SYSTEM FOR APPLICATION SELF-OPTIMIZATION IN SERVERLESS EDGE COMPUTING ENVIRONMENTS

    公开(公告)号:US20230153182A1

    公开(公告)日:2023-05-18

    申请号:US17964170

    申请日:2022-10-12

    CPC classification number: G06F9/543 G06F9/505

    Abstract: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

    REINFORCEMENT-LEARNING BASED SYSTEM FOR CAMERA PARAMETER TUNING TO IMPROVE ANALYTICS

    公开(公告)号:US20220414935A1

    公开(公告)日:2022-12-29

    申请号:US17825519

    申请日:2022-05-26

    Abstract: A method for automatically adjusting camera parameters to improve video analytics accuracy during continuously changing environmental conditions is presented. The method includes capturing a video stream from a plurality of cameras, performing video analytics tasks on the video stream, the video analytics tasks defined as analytics units (AUs), applying image processing to the video stream to obtain processed frames, filtering the processed frames through a filter to discard low-quality frames and dynamically fine-tuning parameters of the plurality of cameras. The fine-tuning includes passing the filtered frames to an AU-specific proxy quality evaluator, employing State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) computations to automatically fine-tune the parameters of the plurality of cameras, and based on the reinforcement computations, applying a new policy for an agent to take actions and learn to maximize a reward.

    DYNAMIC MICROSERVICE INTERCOMMUNICATION CONFIGURATION

    公开(公告)号:US20220337644A1

    公开(公告)日:2022-10-20

    申请号:US17720776

    申请日:2022-04-14

    Abstract: Methods and systems for managing communications include identifying a system condition in a distributed computing system comprising a first microservice in communication with a second microservice. A communications method is identified responsive to the identified system condition using a reinforcement learning model that associates communication methods with system conditions. The identified communications method is implemented for communications between the first microservice and the second microservice, such that the first microservice and the second microservice use the identified communications method to transmit data.

    SELF-OPTIMIZING VIDEO ANALYTICS PIPELINES

    公开(公告)号:US20220172005A1

    公开(公告)日:2022-06-02

    申请号:US17522226

    申请日:2021-11-09

    Abstract: A method for implementing a self-optimized video analytics pipeline is presented. The method includes decoding video files into a sequence of frames, extracting features of objects from one or more frames of the sequence of frames of the video files, employing an adaptive resource allocation component based on reinforcement learning (RL) to dynamically balance resource usage of different microservices included in the video analytics pipeline, employing an adaptive microservice parameter tuning component to balance accuracy and performance of a microservice of the different microservices, applying a graph-based filter to minimize redundant computations across the one or more frames of the sequence of frames, and applying a deep-learning-based filter to remove unnecessary computations resulting from mismatches between the different microservices in the video analytics pipeline.

    ECO: EDGE-CLOUD OPTIMIZATION OF 5G APPLICATIONS

    公开(公告)号:US20220150326A1

    公开(公告)日:2022-05-12

    申请号:US17515875

    申请日:2021-11-01

    Abstract: A method for optimal placement of microservices of a micro-services-based application in a multi-tiered computing network environment employing 5G technology is presented. The method includes accessing a centralized server or cloud to request a set of services to be deployed on a plurality of sensors associated with a plurality of devices, the set of services including launching an application on a device of the plurality of devices, modeling the application as a directed graph with vertices being microservices and edges representing communication between the microservices, assigning each of the vertices of the directed graph with two cost weights, employing an edge monitor (EM), an edge scheduler (ES), an alerts-manager at edge (AM-E), and a file transfer (FT) at the edge to handle partitioning of the microservices, and dynamically mapping the microservices to the edge or the cloud to satisfy application-specific response times.

    Online face clustering
    58.
    发明授权

    公开(公告)号:US11250244B2

    公开(公告)日:2022-02-15

    申请号:US16814453

    申请日:2020-03-10

    Abstract: Methods and systems for image clustering include matching a new image to a representative image of a cluster. The new image is set as a representative of the cluster with a first time limit. The new image is set as a representative of the cluster with a second time limit, responsive to a determination that the new image has matched at least one incoming image during the first time limit.

    Person search system based on multiple deep learning models

    公开(公告)号:US11250243B2

    公开(公告)日:2022-02-15

    申请号:US16808983

    申请日:2020-03-04

    Abstract: A computer-implemented method executed by at least one processor for person identification is presented. The method includes employing one or more cameras to receive a video stream including a plurality of frames to extract features therefrom, detecting, via an object detection model, objects within the plurality of frames, detecting, via a key point detection model, persons within the plurality of frames, detecting, via a color detection model, color of clothing worn by the persons, detecting, via a gender and age detection model, an age and a gender of the persons, establishing a spatial connection between the objects and the persons, storing the features in a feature database, each feature associated with a confidence value, and normalizing, via a ranking component, the confidence values of each of the features.

    Usecase specification and runtime execution

    公开(公告)号:US11249803B2

    公开(公告)日:2022-02-15

    申请号:US16809154

    申请日:2020-03-04

    Abstract: A computer-implemented method includes obtaining a usecase specification and a usecase runtime specification corresponding to the usecase. The usecase includes a plurality of applications each being associated with a micro-service providing a corresponding functionality within the usecase for performing a task. The method further includes determining that at least one instance of the at least one of the plurality of applications can be reused during execution of the usecase based on the usecase specification and the usecase runtime specification, and reusing the at least one instance during execution of the usecase.

Patent Agency Ranking