This article describes how to use a mobile robot with a live streaming camera to implement various use cases. The solution implements a system that runs locally on Azure Stack Edge to ingest and process the video stream and Azure AI services that perform object detection.
Architecture
Download a Visio file of this architecture.
Workflow
This workflow describes how the system processes the incoming data:
A camera that's installed on the robot streams video in real time by using Real Time Streaming Protocol (RTSP).
A container in the Kubernetes cluster on Azure Stack Edge reads the incoming stream and splits video into separate images. An open-source software tool called FFmpeg ingests and processes the video stream.
Images are stored in the local Azure Stack Edge storage account.
Each time a new key frame is saved in the storage account, an AI Vision container picks it up. For information about the separation of logic into multiple containers, see Scenario details.
When it loads a key frame from the storage container, the AI Vision container sends it to Azure AI services in the cloud. This architecture uses Azure AI Vision, which enables object detection via image analysis.
The results of image analysis (detected objects and a confidence rating) are sent to the anomaly detection container.
The anomaly detection container stores the results of image analysis and anomaly detection in the local Azure SQL Database instance of Azure Stack Edge for future reference. Using a local instance of the database improves access time, which helps to minimize delays in data access.
Data processing is run to detect any anomalies in the incoming real-time video stream. If anomalies are detected, a front-end UI shows an alert.
Components
Azure Stack Edge is used to host running Azure services on-premises, close to the location where anomaly detection occurs, which reduces latency.
Azure Kubernetes Service on Azure Stack Edge is used to run a Kubernetes cluster of containers that contain the system's logic on Azure Stack Edge in a simple and managed way.
Azure Arc controls the Kubernetes cluster that runs on the edge device.
Azure AI Vision is used to detect objects in key frames of the video stream.
Azure Blob Storage is used to store images of key frames that are extracted from the video stream.
Azure SQL Edge is used to store data on the edge, close to the service that consumes and processes it.
Azure Container Registry is used to store Docker container images.
Azure Key Vault provides enhanced-security storage for any secrets or cryptographic keys that are used by the system.
Azure Monitor provides observability for the system.
Scenario details
This architecture demonstrates a system that processes a real-time video stream, compares the extracted real-time data with a set of reference data, and makes decisions based on the results. For example, it could be used to provide scheduled inspections of a fenced perimeter around a secured location.
The architecture uses Azure Stack Edge to ensure that the most resource-intensive processes are performed on-premises, close to the source of the video. This design significantly improves the response time of the system, which is important when an immediate response to an anomaly is critical.
Because the parts of the system are deployed as independent containers in a Kubernetes cluster, you can scale only the required subsystems according to demand. For example, if you increase the number of cameras for the video feed, you can scale the container that's responsible for video ingestion and processing to handle the demand but keep the rest of the cluster at the original level.
Offloading the object detection functionality to Azure AI services significantly reduces the expertise that you need to deploy this architecture. Unless your requirements for object detection are highly specialized, the out-of-the-box approach you get from the Image Analysis service is sufficient and doesn't require knowledge of machine learning.
Potential use cases
Monitoring the security of a perimeter
Detecting an unsafe working environment in a factory
Detecting anomalies in an automated assembly line
Detecting a lack of de-icing fluid on aircraft
Considerations
These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that you can use to improve the quality of a workload. For more information, see Microsoft Azure Well-Architected Framework.
Reliability
Reliability ensures your application can meet the commitments you make to your customers. For more information, see Overview of the reliability pillar.
One of the biggest advantages of using Azure Stack Edge is that you get fully managed components on your on-premises hardware. All fully managed Azure components are automatically resilient at a regional level.
In addition, running the system in a Kubernetes cluster enables you to offload the responsibility for keeping the subsystems healthy to the Kubernetes orchestration system.
Security
Security provides assurances against deliberate attacks and the abuse of your valuable data and systems. For more information, see Overview of the security pillar.
Microsoft Entra managed identities provide security for all components of this architecture. Using managed identities eliminates the need to store secrets in code or configuration files. It simplifies access control, credential management, and role assignment.
Cost optimization
Cost optimization is about reducing unnecessary expenses and improving operational efficiencies. For more information, see Overview of the cost optimization pillar.
To see a pricing example for this scenario, use the Azure pricing calculator. The most expensive components in the scenario are Azure Stack Edge and Azure Kubernetes Service. These services provide capacity for scaling the system to address increased demand in the future.
The cost of using Azure AI services for object detection varies based on how long the system runs. The preceding pricing example is based on a system that produces one image per second and operates for 8 hours per day. One FPS is sufficient for this scenario. However, if your system needs to run for longer periods of time, the cost of using Azure AI services is higher:
Performance efficiency
Performance efficiency is the ability of your workload to scale to meet the demands placed on it by users in an efficient manner. For more information, see Performance efficiency pillar overview.
Because the code is deployed in a Kubernetes cluster, you can take advantage of the benefits of this powerful orchestration system. Because the various subsystems are separated into containers, you can scale only the most demanding parts of the application. At a basic level, with one incoming video feed, the system can contain just one node in a cluster. This design significantly simplifies the initial configuration. As demand for data processing grows, you can easily scale the cluster by adding nodes.
Contributors
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal author:
- Nick Sologoub | Principal Software Engineering Lead
Other contributors:
- Mick Alberts | Technical Writer
- Frédéric Le Coquil | Principal Software Engineer
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Next steps
Product documentation:
- Object detection
- Responsible use of AI
- What is Azure Stack Edge Pro 2?
- Azure Kubernetes Service
- Azure Arc overview
Guided learning path:
- Bring Azure innovation to your hybrid environments with Azure Arc
- Introduction to Azure Kubernetes Service
- Introduction to Azure Stack
- Analyze images with the Computer Vision service