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Parking Management using Ultralytics YOLOv8 🚀

What is Parking Management System?

Parking management with Ultralytics YOLOv8 ensures efficient and safe parking by organizing spaces and monitoring availability. YOLOv8 can improve parking lot management through real-time vehicle detection, and insights into parking occupancy.

Advantages of Parking Management System?

  • Efficiency: Parking lot management optimizes the use of parking spaces and reduces congestion.
  • Safety and Security: Parking management using YOLOv8 improves the safety of both people and vehicles through surveillance and security measures.
  • Reduced Emissions: Parking management using YOLOv8 manages traffic flow to minimize idle time and emissions in parking lots.

Real World Applications

Parking Management System Parking Management System
Parking lots Analytics Using Ultralytics YOLOv8 Parking management top view using Ultralytics YOLOv8
Parking management Aeriel View using Ultralytics YOLOv8 Parking management Top View using Ultralytics YOLOv8

Parking Management System Code Workflow

Selection of Points

Point Selection is now Easy

Choosing parking points is a critical and complex task in parking management systems. Ultralytics streamlines this process by providing a tool that lets you define parking lot areas, which can be utilized later for additional processing.

  • Capture a frame from the video or camera stream where you want to manage the parking lot.
  • Use the provided code to launch a graphical interface, where you can select an image and start outlining parking regions by mouse click to create polygons.

Image Size

Max Image Size of 1920 * 1080 supported

from import ParkingPtsSelection, tk

root = tk.Tk()
  • After defining the parking areas with polygons, click save to store a JSON file with the data in your working directory.

Ultralytics YOLOv8 Points Selection Demo

Python Code for Parking Management

Parking management using YOLOv8 Example

import cv2
from ultralytics import solutions

# Path to json file, that created with above point selection app
polygon_json_path = "bounding_boxes.json"

# Video capture
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
video_writer = cv2.VideoWriter("parking management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Initialize parking management object
management = solutions.ParkingManagement(model_path="")

while cap.isOpened():
    ret, im0 =
    if not ret:

    json_data = management.parking_regions_extraction(polygon_json_path)
    results = management.model.track(im0, persist=True, show=False)

    if results[0] is not None:
        boxes = results[0].boxes.xyxy.cpu().tolist()
        clss = results[0].boxes.cls.cpu().tolist()
        management.process_data(json_data, im0, boxes, clss)



Optional Arguments ParkingManagement

Name Type Default Description
model_path str None Path to the YOLOv8 model.
txt_color tuple (0, 0, 0) RGB color tuple for text.
bg_color tuple (255, 255, 255) RGB color tuple for background.
occupied_region_color tuple (0, 255, 0) RGB color tuple for occupied regions.
available_region_color tuple (0, 0, 255) RGB color tuple for available regions.
margin int 10 Margin for text display.

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 2024-04-29, Updated 2024-05-18
Authors: glenn-jocher (2), RizwanMunawar (2)