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| 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. | |
| <p align="center"> | |
| <br> | |
| <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/rCggzXRRSRo" | |
| title="YouTube video player" frameborder="0" | |
| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" | |
| allowfullscreen> | |
| </iframe> | |
| <br> | |
| <strong>Watch:</strong> Speed Estimation using Ultralytics YOLOv8 | |
| </p> | |
| ## 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 | | |