The gst-udf-loader gstreamer plugin supports loading and execution of python and native(c++) UDFs.
#Edge pipeline app zip
The following configuration files, scripts and tools used with the Edge Video Analytics Microservice are included in the Edge Video Analytics Resources zip file: This is a stripped-down version of Edge Video Analytics Microservice and has no Edge Insights for Industrial components like message bus publisher and gst-udfloader element installed, and hence has a smaller container size.įigure 2: Architecture Diagram - Standalone Version Edge Video Analytics Microservice Resources NOTE: Optionally, one can also run the standalone version of the microservice by downloading the docker image from Docker hub.
#Edge pipeline app software
For the REST API definition, refer to the RESTful Microservice interface.Įdge Insights for Industrial (EII) Mode: Supports EII Configuration Manager for pipeline execution and EII Message Bus for publishing of inference results, making it compatible with the Edge Insights for Industrial software stack. The microservice can be started in one of two modes – Edge Insights Industrial (EII) to deploy with EII software stack or Edge Video Analytics (EVA) to deploy independent of the EII stack.Įdge Video Analytics (EVA) Mode: Provides the same RESTful APIs as Intel® DL Streamer Pipeline Server to discover, start, stop, customize, and monitor pipeline execution and supports MQTT and Kafka message brokers for publishing the inference results.
The Docker image uses Intel® DL Streamer Pipeline Server as a library. The pipelines run by the microservice are defined in GStreamer* using Intel® DL Streamer Pipeline Server for inferencing. This is a Python* microservice used for deploying optimized video analytics pipelines and is provided as a Docker image in the package. How It Works Edge Video Analytics Microservice † Use Kernel 5.8 for 11th generation Intel® Core™ processors. Refer to OpenVINO™ Toolkit System Requirements for supported GPU and VPU processors. Intel Atom® processor with Intel® Streaming SIMD Extensions 4.2 (Intel® SSE4.2).1st to 3rd generation of Intel® Xeon® Scalable processors.6th to 11th generation Intel® Core™ processors.Deep Learning Streamer (Intel® DL Streamer) Pipeline Server.
#Edge pipeline app download
Select Configure & Download to download the microservice and the software listed below. The microservices can be deployed independently or with the Edge Insights for Industrial (EII) software stack to perform video analytics on the edge devices.ĭevelopers can save development and deployment time by using the pre-built Docker* image and by simply configuring the video analytics pipelines in the well-known JSON format. The pre-built container images provided by the package allow developers to replace the deep learning models and pipelines used in the container with their own deep learning models and pipelines. This use case features interoperable containerized microservices for developing and deploying optimized video analytics pipelines built using Intel® DL Streamer as an inferencing backend. The algorithms used for video analytics perform object detection, classification, identification, counting, and tracking on the input video stream. It is used in business domains such as healthcare, retail, entertainment and industrial. Video Analytics refers to transforming video streams into insights through video processing, inference, and analytics operations.
0 kommentar(er)
