Windows IoT Retail Analytics Project

Technical Architecture

The solution comprises several key components:

Edge Layer (Windows IoT Device)

• Hardware: Intel NUC or similar small form-factor PC running Windows IoT Enterprise.
• Sensors: USB cameras (e.g., Azure Kinect DK, standard webcams), depth sensors, potentially RFID readers.
• Software:
  • .NET Core / C# applications for data acquisition and processing.
  • Azure IoT Edge runtime for module deployment and device management.
  • Machine Learning models (e.g., trained with Azure ML, TensorFlow, PyTorch) for object detection, pose estimation, and classification.
  • Local data buffering and pre-processing.

Cloud Layer (Azure)

• Azure IoT Hub: Secure bi-directional communication between edge devices and the cloud.
• Azure Stream Analytics: Real-time data processing and aggregation.
• Azure Machine Learning: Model training, deployment, and management.
• Azure SQL Database / Cosmos DB: Storing processed data and analytics results.
• Power BI: Interactive dashboards and reporting for business insights.
• Azure Blob Storage: Storing raw video feeds or images for archival or further analysis.

Data Flow: Sensors capture data -> Edge device processes data using ML models -> Processed insights are sent to IoT Hub -> Stream Analytics processes real-time data -> Results are stored and visualized in Power BI.

Example Code Snippet (C# - Edge Device)

// Example of capturing frames from a camera and performing basic processing
using System.Drawing;
using System.IO;
using VideoCaptureLib; // Placeholder for a camera capture library

public class RetailAnalyticsEdge
{
    private VideoCapture _capture;

    public RetailAnalyticsEdge()
    {
        _capture = new VideoCapture(0); // Initialize with default camera
    }

    public void ProcessCameraFeed()
    {
        if (_capture.Start())
        {
            while (true)
            {
                Bitmap frame = _capture.QueryFrame();
                if (frame != null)
                {
                    // --- Edge Processing Logic ---
                    // 1. Perform object detection (e.g., detect people)
                    var detections = DetectObjects(frame);

                    // 2. Count people entering/exiting based on zones (requires defining zones)
                    var countData = AnalyzeDetections(detections);

                    // 3. Send processed data to Azure IoT Hub (implementation omitted for brevity)
                    SendToCloud(countData);

                    frame.Dispose(); // Release frame resources
                }
                // Introduce a small delay to control frame rate
                System.Threading.Thread.Sleep(50);
            }
        }
        else
        {
            Console.WriteLine("Failed to start camera.");
        }
    }

    private object DetectObjects(Bitmap frame)
    {
        // Placeholder for your ML model inference logic
        // e.g., using ONNX Runtime, TensorFlow Lite, or Azure ML inference client
        Console.WriteLine("Performing object detection...");
        // Return a list of detected objects, their bounding boxes, and labels
        return new { PeopleCount = 5 }; // Simplified output
    }

    private object AnalyzeDetections(object detections)
    {
        // Placeholder for analyzing detections to determine entry/exit counts,
        // queue lengths, etc.
        Console.WriteLine("Analyzing detections...");
        // This would involve comparing current frame detections with previous frames
        // and applying zone logic.
        return new { Ingress = 1, Egress = 0 }; // Simplified output
    }

    private void SendToCloud(object data)
    {
        // Placeholder for sending data to Azure IoT Hub
        // e.g., using Azure.Messaging.IoTHub.IoTHubDeviceClient
        Console.WriteLine($"Sending to cloud: {data}");
    }

    public void Stop()
    {
        _capture.Stop();
    }
}