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

comments	description	keywords
true	Queue Management Using Ultralytics YOLOv8	Ultralytics, YOLOv8, Queue Management, Object Counting, Object Tracking, Object Detection, Notebook, IPython Kernel, CLI, Python SDK

Queue Management using Ultralytics YOLOv8 🚀

What is Queue Management?

Queue management using Ultralytics YOLOv8 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.

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 World Applications

Logistics	Retail
Queue management at airport ticket counter Using Ultralytics YOLOv8	Queue monitoring in crowd Ultralytics YOLOv8

!!! Example "Queue Management using YOLOv8 Example"

=== "Queue Manager"

    ```python
    import cv2
    from ultralytics import YOLO, solutions

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")

    assert cap.isOpened(), "Error reading video file"
    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))

    queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]

    queue = solutions.QueueManager(
        classes_names=model.names,
        reg_pts=queue_region,
        line_thickness=3,
        fontsize=1.0,
        region_color=(255, 144, 31),
    )

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

        if success:
            tracks = model.track(im0, show=False, persist=True, verbose=False)
            out = queue.process_queue(im0, tracks)

            video_writer.write(im0)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
            continue

        print("Video frame is empty or video processing has been successfully completed.")
        break

    cap.release()
    cv2.destroyAllWindows()
    ```

=== "Queue Manager Specific Classes"

    ```python
    import cv2
    from ultralytics import YOLO, solutions

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")

    assert cap.isOpened(), "Error reading video file"
    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))

    queue_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]

    queue = solutions.QueueManager(
        classes_names=model.names,
        reg_pts=queue_region,
        line_thickness=3,
        fontsize=1.0,
        region_color=(255, 144, 31),
    )

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

        if success:
            tracks = model.track(im0, show=False, persist=True, verbose=False, classes=0)  # Only person class
            out = queue.process_queue(im0, tracks)

            video_writer.write(im0)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
            continue

        print("Video frame is empty or video processing has been successfully completed.")
        break

    cap.release()
    cv2.destroyAllWindows()
    ```

Arguments QueueManager

Name	Type	Default	Description
classes_names	dict	model.names	A dictionary mapping class IDs to class names.
reg_pts	list of tuples	[(20, 400), (1260, 400)]	Points defining the counting region polygon. Defaults to a predefined rectangle.
line_thickness	int	2	Thickness of the annotation lines.
track_thickness	int	2	Thickness of the track lines.
view_img	bool	False	Whether to display the image frames.
region_color	tuple	(255, 0, 255)	Color of the counting region lines (BGR).
view_queue_counts	bool	True	Whether to display the queue counts.
draw_tracks	bool	False	Whether to draw tracks of the objects.
count_txt_color	tuple	(255, 255, 255)	Color of the count text (BGR).
track_color	tuple	None	Color of the tracks. If None, different colors will be used for different tracks.
region_thickness	int	5	Thickness of the counting region lines.
fontsize	float	0.7	Font size for the text annotations.

Arguments model.track

Name	Type	Default	Description
source	im0	None	source directory for images or videos
persist	bool	False	persisting tracks between frames
tracker	str	botsort.yaml	Tracking method 'bytetrack' or 'botsort'
conf	float	0.3	Confidence Threshold
iou	float	0.5	IOU Threshold
classes	list	None	filter results by class, i.e. classes=0, or classes=[0,2,3]
verbose	bool	True	Display the object tracking results