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title: Accident_Detection_App
emoji: π
colorFrom: blue
colorTo: red
sdk: streamlit
app_file: accident_app.py
pinned: false
Accident Detection Model
This application showcases the capabilities of our Accident Detection Model, a pivotal component of our research project focused on Accident Detection within Smart City Transportation frameworks.
Overview
The application empowers users to view a selection of sample accident videos and upload a new video to test the model. Our model is adept at detecting accidents in both trimmed and untrimmed video formats.
Table of Contents
Installation
Clone the repository:
git clone [(https://github.com/adewopova/Accident_detection_SM_City/)]
Navigate to the directory:
cd path_to_diretory
Install the required dependencies:
pip install -r requirements.txt
Launch the Streamlit app:
streamlit run app.py
Usage
With the app up and running:
- Opt between trimmed and untrimmed video variants.
- Pick a sample video from the provided list or upload a video of your choice.
- The model will analyze the video and superimpose accident likelihood indicators.
Features
- Sample Videos: Preloaded sample videos for immediate testing.
- Accident Prediction: The core functionality that exhibits the probability of an accident occurrence within the selected video.
- User-friendly Interface: Crafted using Streamlit, ensuring a seamless and intuitive user experience.
Contribution
Your contributions can make a difference! Kindly consult the contribution guidelines prior to submitting any changes.
License
This project is protected under the MIT License. For more details, please refer to the LICENSE.md
file.
Acknowledgments
A heartfelt appreciation to our dedicated research team members: Victor Adewopo and Nelly Elsayed.