https://store-images.s-microsoft.com/image/apps.26498.1641bf5d-0d58-4452-9790-1521a6204889.e05401d2-2b52-4616-b7ec-8e30f45e802d.0f756717-3287-42c8-9ae6-e3cc733bb431

Dace IT℠Edge Video Analytics Micro SaaS 2024

by Dace IT℠d/b/a Sense Traffic Pulse™

Dace IT℠Edge Video Analytics Microservices SaaS

Dace IT℠ Edge Video Analytics Micro SaaS 2024

  • Description: This SaaS offers a powerful solution for intelligent video analytics and media analysis. Using computer vision, it provides insights for immediate action by analyzing video metadata. The service includes containerized microservices for developing and deploying video analytics pipelines, utilizing Intel® DL Streamer as the inferencing backend.
  • New Features: Now integrated with Microsoft Copilot and Open AI, it offers enhanced capabilities for object detection, classification, and tracking, ensuring more accurate and insightful video analytics.
  • Use Case: Ideal for surveillance, healthcare, retail, and industrial applications.
  • How It Works: It operates in two modes - Edge Insights Industrial (EII) and Edge Insights Video (EVA), offering flexibility in deployment and compatibility with various software stacks​​.
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.

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 inference. 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.

At a glance

https://store-images.s-microsoft.com/image/apps.8321.1641bf5d-0d58-4452-9790-1521a6204889.e05401d2-2b52-4616-b7ec-8e30f45e802d.4ee4df50-7fa4-493e-b98e-301e32ac4b65
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https://store-images.s-microsoft.com/image/apps.19255.1641bf5d-0d58-4452-9790-1521a6204889.d3e36cdb-f81a-45dc-9fa1-bf6f3285c317.e68b0596-c300-49a1-8030-06012a45054f
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https://store-images.s-microsoft.com/image/apps.41866.1641bf5d-0d58-4452-9790-1521a6204889.d3e36cdb-f81a-45dc-9fa1-bf6f3285c317.35e3faea-6dd5-4e7a-882a-60cb13bb27ea
https://store-images.s-microsoft.com/image/apps.38042.1641bf5d-0d58-4452-9790-1521a6204889.d3e36cdb-f81a-45dc-9fa1-bf6f3285c317.fdd4e010-b285-42c5-b9c3-9f90bb70cf19
https://store-images.s-microsoft.com/image/apps.50732.1641bf5d-0d58-4452-9790-1521a6204889.d3e36cdb-f81a-45dc-9fa1-bf6f3285c317.3ca877a3-1ad8-450e-bb25-bd55b51e0fec