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Speed Estimation using Ultralytics YOLOv8 🚀

What is Speed Estimation?

Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using Ultralytics YOLOv8 you can now calculate the speed of object using object tracking alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.



Watch: Speed Estimation using Ultralytics YOLOv8

Advantages of Speed Estimation?

  • Efficient Traffic Control: Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.
  • Precise Autonomous Navigation: In autonomous systems like self-driving cars, reliable speed estimation ensures safe and accurate vehicle navigation.
  • Enhanced Surveillance Security: Speed estimation in surveillance analytics helps identify unusual behaviors or potential threats, improving the effectiveness of security measures.

Real World Applications

Transportation Transportation
Speed Estimation on Road using Ultralytics YOLOv8 Speed Estimation on Bridge using Ultralytics YOLOv8
Speed Estimation on Road using Ultralytics YOLOv8 Speed Estimation on Bridge using Ultralytics YOLOv8

Speed Estimation using YOLOv8 Example

from ultralytics import YOLO
from ultralytics.solutions import speed_estimation
import cv2

model = YOLO("yolov8n.pt")
names = model.model.names

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("speed_estimation.avi",
                               cv2.VideoWriter_fourcc(*'mp4v'),
                               fps,
                               (w, h))

line_pts = [(0, 360), (1280, 360)]

# Init speed-estimation obj
speed_obj = speed_estimation.SpeedEstimator()
speed_obj.set_args(reg_pts=line_pts,
                   names=names,
                   view_img=True)

while cap.isOpened():

    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    tracks = model.track(im0, persist=True, show=False)

    im0 = speed_obj.estimate_speed(im0, tracks)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()
Speed is Estimate

Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary depending on GPU speed.

Optional Arguments set_args

Name Type Default Description
reg_pts list [(20, 400), (1260, 400)] Points defining the Region Area
names dict None Classes names
view_img bool False Display frames with counts
line_thickness int 2 Increase bounding boxes thickness
region_thickness int 5 Thickness for object counter region or line
spdl_dist_thresh int 10 Euclidean Distance threshold for speed check line

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 2024-01-05, Updated 2024-03-02
Authors: chr043416@gmail.com (2), glenn-jocher (2), AyushExel (1)

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