Comments (3)
Hey! 👋
To tackle extracting car snippets based on speed estimation with YOLOv8, you'd typically first need to detect the cars and then estimate their speed. Here's a simplified approach:
-
Detect cars using YOLOv8. Ensure your model is trained for car detection or use a pre-trained model that can detect cars.
-
Estimate Speed: This can be complex, requiring additional steps like:
- Establishing a frame of reference in your videos.
- Calculating the pixel distance traveled by cars between frames.
- Converting this to real-world speed (you might need camera calibration and frame rate data for this).
-
Extract snippets: Once cars exceeding a certain speed are identified, extract these video snippets for further use or analysis.
Here’s a basic snippet for detecting cars with YOLOv8:
from ultralytics import YOLO
# Load the pretrained model
model = YOLO('yolov8n.pt')
# Run detection
results = model('traffic_video.mp4')
# Processing the results
for frame in results:
for *xyxy, conf, cls in frame.pred:
if model.names[int(cls)] == 'car' and meets_speed_criteria(xyxy):
save_frame(frame) # Define your function to check speed and save frames
You'd need to develop the meets_speed_criteria
function based on how you compute speed.
Since specific implementations can vary greatly, further reading on motion analysis and possibly consulting with a computer vision expert could be very beneficial. Always here to help if you need more info! 🚗💨
from ultralytics.
👋 Hello @AbdullahHabib-github, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
from ultralytics.
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
from ultralytics.
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