ultralytics/docs/en/guides/queue-management.md

comments	description	keywords
true	Learn how to manage and optimize queues using Ultralytics YOLO11 to reduce wait times and increase efficiency in various real-world applications.	queue management, YOLO11, Ultralytics, reduce wait times, efficiency, customer satisfaction, retail, airports, healthcare, banks

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 Implement Queue Management with Ultralytics YOLO11 | Airport and Metro Station

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

!!! example "Queue Management using Ultralytics YOLO"

=== "CLI"

    ```bash
    # 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)]"
    ```

=== "Python"

    ```python
    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:

{% from "macros/solutions-args.md" import param_table %} {{ param_table(["model", "region"]) }}

The QueueManagement solution also support some track arguments:

{% from "macros/track-args.md" import param_table %} {{ param_table(["tracker", "conf", "iou", "classes", "verbose", "device"]) }}

Additionally, the following visualization parameters are available:

{% from "macros/visualization-args.md" import param_table %} {{ param_table(["show", "line_width", "show_conf", "show_labels"]) }}

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.