metadata

title: Accident_Detection_App
emoji: 😏
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.

Installation
Usage
Features
Contribution
License
Acknowledgments

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.

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

Spaces:

adewopova
/

Accident_Detection_App

Sleeping