Dace IT Edge Video Analytics Micro SaaS

Democratizing Intelligent Video Analytics!

Dace IT℠- Empowering People with Computer Vision and On-Demand Intelligent Video Analytics & Media Analysis solutions that detect, analyze, count, engage and work with IoT that reflect insights for immediate action.

Our mission is to empower customers of all sizes and types with computer-vision and on-demand intelligent video analytics solutions! We are deeply focused on getting hidden actionable insight in video metadata while boosting the productivity of people who analyze video and media. Innovations enabling digital transformation Innovations enabling new opportunities Iot Projects don't have to be complex.

Video Analytics refers to transforming video streams into insights through video processing, inference, and analytics operations. It is used in a wide range of business domains such as video surveillance, healthcare, retail, entertainment and industrial. The algorithms used for video analytics perform object detection, classification, identification, counting, and tracking on the input video stream.

This Video Analytic SaaS features interoperable containerized micro services for developing and deploying optimized video analytics pipelines built using Intel® DL Streamer as inferencing backend. Our 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.

These micro services can be used independently or with Edge Insights for Industrial (EII) software stack to perform video analytics on the edge devices. Consumers can save time by using this Edge Video Analytics Micro services by simply configuring the video analytics pipelines in the well-known JSON format.

How It Works

Edge Video Analytics Microservice

This is a Python* micro-services used for deploying optimized video analytics pipelines and is provided as a Docker image in the package. The pipelines run by the micro services are defined in GStreamer* using Intel® DL Streamer for inferencing. The Docker image uses Intel® DL Streamer Pipeline Server as a library. The micro services can be started in one of two modes – Edge Insights Industrial (EII) to deploy with EII software stack or Edge Insights Video (EVA) to deploy independent of the EII stack.

Edge Video Analytics (EVA) Mode: Provides the same RESTful APIs as Video Analytics Serving to discover, start, stop, customize, and monitor pipeline execution and supports MQTT and Kafka message brokers for publishing the inference results. For REST API definition, refer to the RESTful Microservice interface.

Edge 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 Edge Insights for Industrial software stack.

Solutions Brief