APPLICATION-IN-A-BOX FOR DEPLOYMENT AND SELF-OPTIMIZATION OF REMOTE APPLICATIONS

    公开(公告)号:US20240314531A1

    公开(公告)日:2024-09-19

    申请号:US18605105

    申请日:2024-03-14

    CPC classification number: H04W4/50 H04W24/02 H04W72/52

    Abstract: Systems and methods are provided for deploying applications within a wireless network infrastructure, including initiating, by a centralized control module in a pre-configured hardware unit having a 5G wireless communication module, edge computing device, centralized control module, and data processing module with access to cloud resources, a setup procedure upon receiving a deployment command, the setup procedure including activating the 5G wireless communication module to establish a network connection. User equipment for communication with sensors and cameras is deployed using an edge device through the network connection. Application deployment is managed using a centralized control module including an edge cloud optimizer for allocating resources between an edge computing device and the cloud resources based on real-time analysis of network conditions and application requirements. Computing resource allocation between the edge computing device and cloud resources is dynamically adjusted for application requirements and network conditions during automated application deployment and optimization.

    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.

    Tracking within and across facilities

    公开(公告)号:US11468576B2

    公开(公告)日:2022-10-11

    申请号:US17178570

    申请日:2021-02-18

    Abstract: A method for tracing individuals through physical spaces that includes registering cameras in groupings relating a physical space. The method further includes performing local video monitoring including a video sensor input that outputs frames from inputs from recording with the cameras in the groupings, a face detection application for extracting faces from the output frames, and a face matching application for matching faces extracted from the output frames to a watchlist, and a local movement monitor that assigns tracks to the matched faces. The method further includes performing a global monitor including a biometrics monitor for preparing the watchlist of faces, the watchlist of faces being updated when a new face is detected by the cameras in the groupings, and a global movement monitor that combines the outputs from the assigned tracks to the matched faces to launch a report regarding individual population traveling to the physical spaces.

    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.

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