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.

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.

• Installation
• Usage
• Features
• Contribution
• License
• Acknowledgments

1. Clone the repository:

   git clone [(https://github.com/adewopova/Accident_detection_SM_City/)]

2. Navigate to the directory:

   cd path_to_diretory

3. Install the required dependencies:

   pip install -r requirements.txt

4. Launch the Streamlit app:

   streamlit run app.py

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.

• 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.

Your contributions can make a difference! Kindly consult the contribution guidelines prior to submitting any changes.

This project is protected under the MIT License. For more details, please refer to the LICENSE.md file.

A heartfelt appreciation to our dedicated research team members: Victor Adewopo and Nelly Elsayed.

https://arxiv.org/pdf/2310.10038.pdf