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99 lines
6.1 KiB
99 lines
6.1 KiB
11 months ago
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---
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comments: true
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description: Speed Estimation Using Ultralytics YOLOv8
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keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
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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 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?
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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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from ultralytics import YOLO
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from ultralytics.solutions import speed_estimation
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import cv2
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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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# Video writer
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video_writer = cv2.VideoWriter("speed_estimation.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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int(cap.get(5)),
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(int(cap.get(3)), int(cap.get(4))))
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line_pts = [(0, 360), (1280, 360)]
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# Init speed-estimation obj
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speed_obj = speed_estimation.SpeedEstimator()
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speed_obj.set_args(reg_pts=line_pts,
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names=names,
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view_img=True)
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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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### Optional Arguments `set_args`
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| Name | Type | Default | Description |
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|---------------------|-------------|----------------------------|---------------------------------------------------|
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| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| names | `dict` | `None` | Classes names |
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| view_img | `bool` | `False` | Display frames with counts |
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| line_thickness | `int` | `2` | Increase bounding boxes thickness |
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| region_thickness | `int` | `5` | Thickness for object counter region or line |
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| spdl_dist_thresh | `int` | `10` | Euclidean Distance threshold for speed check line |
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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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