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

Queue Management using YOLOv8 Example

import cv2
from ultralytics import YOLO
from ultralytics.solutions import queue_management

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 = queue_management.QueueManager()
queue.set_args(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()

import cv2
from ultralytics import YOLO
from ultralytics.solutions import queue_management

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 = queue_management.QueueManager()
queue.set_args(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()

Optional Arguments set_args

Name Type Default Description
view_img bool False Display frames with counts
view_queue_counts bool True Display Queue counts only on video frame
line_thickness int 2 Increase bounding boxes thickness
reg_pts list [(20, 400), (1260, 400)] Points defining the Region Area
classes_names dict model.model.names Dictionary of Class Names
region_color RGB Color (255, 0, 255) Color of the Object counting Region or Line
track_thickness int 2 Thickness of Tracking Lines
draw_tracks bool False Enable drawing Track lines
track_color RGB Color (0, 255, 0) Color for each track line
count_txt_color RGB Color (255, 255, 255) Foreground color for Object counts text
region_thickness int 5 Thickness for object counter region or line
fontsize float 0.6 Font size of counting text

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


Created 2024-04-02, Updated 2024-04-02
Authors: Burhan-Q (1), RizwanMunawar (1)

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