--- comments: true description: Speed Estimation Using Ultralytics YOLOv8 keywords: 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](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](https://docs.ultralytics.com/modes/track/) 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.



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## 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](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c8a0fd4a-d394-436d-8de3-d5b754755fc7) | ![Speed Estimation on Bridge using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cee10e02-b268-4304-b73a-5b9cb42da669) | | 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 |