e5185ccf63
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
110 lines
6.8 KiB
Markdown
110 lines
6.8 KiB
Markdown
---
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comments: true
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description: Learn how to estimate object speed using Ultralytics YOLOv8 for applications in traffic control, autonomous navigation, and surveillance.
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keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision, traffic control, autonomous navigation, surveillance, security
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---
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# Speed Estimation using Ultralytics YOLOv8 🚀
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## What is Speed Estimation?
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[Speed estimation](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects) 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](../modes/track.md) 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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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/rCggzXRRSRo"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Speed Estimation using Ultralytics YOLOv8
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</p>
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!!! tip "Check Out Our Blog"
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For deeper insights into speed estimation, check out our blog post: [Ultralytics YOLOv8 for Speed Estimation in Computer Vision Projects](https://www.ultralytics.com/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects)
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## Advantages of Speed Estimation?
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- **Efficient Traffic Control:** Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.
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- **Precise Autonomous Navigation:** In autonomous systems like self-driving cars, reliable speed estimation ensures safe and accurate vehicle navigation.
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- **Enhanced Surveillance Security:** Speed estimation in surveillance analytics helps identify unusual behaviors or potential threats, improving the effectiveness of security measures.
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## Real World Applications
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| Transportation | Transportation |
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| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: |
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|  |  |
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| Speed Estimation on Road using Ultralytics YOLOv8 | Speed Estimation on Bridge using Ultralytics YOLOv8 |
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!!! Example "Speed Estimation using YOLOv8 Example"
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=== "Speed Estimation"
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```python
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import cv2
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from ultralytics import YOLO, solutions
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model = YOLO("yolov8n.pt")
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names = model.model.names
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Video writer
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video_writer = cv2.VideoWriter("speed_estimation.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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line_pts = [(0, 360), (1280, 360)]
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# Init speed-estimation obj
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speed_obj = solutions.SpeedEstimator(
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reg_pts=line_pts,
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names=names,
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view_img=True,
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)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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tracks = model.track(im0, persist=True, show=False)
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im0 = speed_obj.estimate_speed(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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???+ warning "Speed is Estimate"
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Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary depending on GPU speed.
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### Arguments `SpeedEstimator`
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| Name | Type | Default | Description |
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| ------------------ | ------ | -------------------------- | ---------------------------------------------------- |
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| `names` | `dict` | `None` | Dictionary of class names. |
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| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | List of region points for speed estimation. |
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| `view_img` | `bool` | `False` | Whether to display the image with annotations. |
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| `line_thickness` | `int` | `2` | Thickness of the lines for drawing boxes and tracks. |
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| `region_thickness` | `int` | `5` | Thickness of the region lines. |
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| `spdl_dist_thresh` | `int` | `10` | Distance threshold for speed calculation. |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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| --------- | ------- | -------------- | ----------------------------------------------------------- |
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| `source` | `im0` | `None` | source directory for images or videos |
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| `persist` | `bool` | `False` | persisting tracks between frames |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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