Skip to content

Register now for

# Queue Management using Ultralytics YOLO11 🚀

## What is Queue Management?

Queue management using Ultralytics YOLO11 involves organizing and controlling lines of people or vehicles to reduce wait times and enhance efficiency. It's about optimizing queues to improve customer satisfaction and system performance in various settings like retail, banks, airports, and healthcare facilities.

Watch: How to Build a Queue Management System with Ultralytics YOLO | Retail, Bank & Crowd Use Cases 🚀

## Advantages of Queue Management

- Reduced Waiting Times: Queue management systems efficiently organize queues, minimizing wait times for customers. This leads to improved satisfaction levels as customers spend less time waiting and more time engaging with products or services.
- Increased Efficiency: Implementing queue management allows businesses to allocate resources more effectively. By analyzing queue data and optimizing staff deployment, businesses can streamline operations, reduce costs, and improve overall productivity.
- Real-time Insights: YOLO11-powered queue management provides instant data on queue lengths and wait times, enabling managers to make informed decisions quickly.
- Enhanced Customer Experience: By reducing frustration associated with long waits, businesses can significantly improve customer satisfaction and loyalty.

## Real World Applications

Logistics | Retail
--- | ---
Queue management at airport ticket counter Using Ultralytics YOLO11 | Queue monitoring in crowd Ultralytics YOLO11

Queue Management using Ultralytics YOLO

CLI Python

    # Run a queue example
    yolo solutions queue show=True

    # Pass a source video
    yolo solutions queue source="path/to/video.mp4"

    # Pass queue coordinates
    yolo solutions queue region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]"

    import cv2

    from ultralytics import solutions

    cap = cv2.VideoCapture("path/to/video.mp4")
    assert cap.isOpened(), "Error reading video file"

    # Video writer
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
    video_writer = cv2.VideoWriter("queue_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    # Define queue points
    queue_region = [(20, 400), (1080, 400), (1080, 360), (20, 360)]  # region points
    # queue_region = [(20, 400), (1080, 400), (1080, 360), (20, 360), (20, 400)]    # polygon points

    # Initialize queue manager object
    queuemanager = solutions.QueueManager(
        show=True,  # display the output
        model="yolo11n.pt",  # path to the YOLO11 model file
        region=queue_region,  # pass queue region points
    )

    # Process video
    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or processing is complete.")
            break
        results = queuemanager(im0)

        # print(results)  # access the output

        video_writer.write(results.plot_im)  # write the processed frame.

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()  # destroy all opened windows

### `QueueManager` Arguments

Here's a table with the `QueueManager` arguments:

Argument | Type | Default | Description
--- | --- | --- | ---
`model` | `str` | `None` | Path to Ultralytics YOLO Model File.
`region` | `list` | `'[(20, 400), (1260, 400)]'` | List of points defining the counting region.

The `QueueManagement` solution also support some `track` arguments:

Argument | Type | Default | Description
--- | --- | --- | ---
`tracker` | `str` | `'botsort.yaml'` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`.
`conf` | `float` | `0.3` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
`iou` | `float` | `0.5` | Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
`classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes.
`verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects.
`device` | `str` | `None` | Specifies the device for inference (e.g., `cpu`, `cuda:0` or `0`). Allows users to select between CPU, a specific GPU, or other compute devices for model execution.

Additionally, the following visualization parameters are available:

Argument | Type | Default | Description
--- | --- | --- | ---
`show` | `bool` | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.
`line_width` | `None or int` | `None` | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.
`show_conf` | `bool` | `True` | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.
`show_labels` | `bool` | `True` | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.

## Implementation Strategies

When implementing queue management with YOLO11, consider these best practices:

1. Strategic Camera Placement: Position cameras to capture the entire queue area without obstructions.
2. Define Appropriate Queue Regions: Carefully set queue boundaries based on the physical layout of your space.
3. Adjust Detection Confidence: Fine-tune the confidence threshold based on lighting conditions and crowd density.
4. Integrate with Existing Systems: Connect your queue management solution with digital signage or staff notification systems for automated responses.

## FAQ

### How can I use Ultralytics YOLO11 for real-time queue management?

To use Ultralytics YOLO11 for real-time queue management, you can follow these steps:

1. Load the YOLO11 model with `YOLO("yolo11n.pt")`.
2. Capture the video feed using `cv2.VideoCapture`.
3. Define the region of interest (ROI) for queue management.
4. Process frames to detect objects and manage queues.

Here's a minimal example:

    import cv2

    from ultralytics import solutions

    cap = cv2.VideoCapture("path/to/video.mp4")
    queue_region = [(20, 400), (1080, 400), (1080, 360), (20, 360)]

    queuemanager = solutions.QueueManager(
        model="yolo11n.pt",
        region=queue_region,
        line_width=3,
        show=True,
    )

    while cap.isOpened():
        success, im0 = cap.read()
        if success:
            results = queuemanager(im0)

    cap.release()
    cv2.destroyAllWindows()

Leveraging Ultralytics HUB can streamline this process by providing a user-friendly platform for deploying and managing your queue management solution.

### What are the key advantages of using Ultralytics YOLO11 for queue management?

Using Ultralytics YOLO11 for queue management offers several benefits:

- Plummeting Waiting Times: Efficiently organizes queues, reducing customer wait times and boosting satisfaction.
- Enhancing Efficiency: Analyzes queue data to optimize staff deployment and operations, thereby reducing costs.
- Real-time Alerts: Provides real-time notifications for long queues, enabling quick intervention.
- Scalability: Easily scalable across different environments like retail, airports, and healthcare.

For more details, explore our Queue Management solutions.

### Why should I choose Ultralytics YOLO11 over competitors like TensorFlow or Detectron2 for queue management?

Ultralytics YOLO11 has several advantages over TensorFlow and Detectron2 for queue management:

- Real-time Performance: YOLO11 is known for its real-time detection capabilities, offering faster processing speeds.
- Ease of Use: Ultralytics provides a user-friendly experience, from training to deployment, via Ultralytics HUB.
- Pretrained Models: Access to a range of pretrained models, minimizing the time needed for setup.
- Community Support: Extensive documentation and active community support make problem-solving easier.

Learn how to get started with Ultralytics YOLO.

### Can Ultralytics YOLO11 handle multiple types of queues, such as in airports and retail?

Yes, Ultralytics YOLO11 can manage various types of queues, including those in airports and retail environments. By configuring the QueueManager with specific regions and settings, YOLO11 can adapt to different queue layouts and densities.

Example for airports:

    queue_region_airport = [(50, 600), (1200, 600), (1200, 550), (50, 550)]
    queue_airport = solutions.QueueManager(
        model="yolo11n.pt",
        region=queue_region_airport,
        line_width=3,
    )

For more information on diverse applications, check out our Real World Applications section.

### What are some real-world applications of Ultralytics YOLO11 in queue management?

Ultralytics YOLO11 is used in various real-world applications for queue management:

- Retail: Monitors checkout lines to reduce wait times and improve customer satisfaction.
- Airports: Manages queues at ticket counters and security checkpoints for a smoother passenger experience.
- Healthcare: Optimizes patient flow in clinics and hospitals.
- Banks: Enhances customer service by managing queues efficiently in banks.

Check our blog on real-world queue management to learn more about how computer vision is transforming queue monitoring across industries.

📅 Created 1 year ago ✏️ Updated 13 days ago

## Comments