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110 lines
6.5 KiB
110 lines
6.5 KiB
--- |
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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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<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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## 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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| 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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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", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, |
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(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 = 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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