ultralytics/docs/en/guides/speed-estimation.md

comments	description	keywords
true	Speed Estimation Using Ultralytics YOLOv8	Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK

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

!!! Example "Speed Estimation using YOLOv8 Example"

=== "Speed Estimation"

    ```python
    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()

    ```

???+ warning "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