Car snippet / Speed estimation about ultralytics HOT 2 OPEN

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:

1. Detect cars using YOLOv8. Ensure your model is trained for car detection or use a pre-trained model that can detect cars.

2. 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).

3. 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! 🚗💨

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👋 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.

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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.

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