https://github.com/valiantlynx/godseye
A campus security app that uses machine vision and a trained model to detect violent and non-violent behavior. By tracking individuals with YOLO and using TensorFlow + LSTM, it alerts security when hostile actions are detected, enabling early intervention to prevent escalation and ensure campus safety.
https://github.com/valiantlynx/godseye
keypoint-detection machine-vision violence-detection
Last synced: about 2 months ago
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A campus security app that uses machine vision and a trained model to detect violent and non-violent behavior. By tracking individuals with YOLO and using TensorFlow + LSTM, it alerts security when hostile actions are detected, enabling early intervention to prevent escalation and ensure campus safety.
- Host: GitHub
- URL: https://github.com/valiantlynx/godseye
- Owner: valiantlynx
- License: other
- Created: 2024-10-17T20:00:02.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-27T20:01:06.000Z (6 months ago)
- Last Synced: 2025-04-27T21:18:40.718Z (6 months ago)
- Topics: keypoint-detection, machine-vision, violence-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 186 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# godseye
This project implements a real-time violence detection system using **Pose Estimation** and **LSTM**. We use two benchmark datasets, **RWF-2000** and **Hockey Fight**, to train and evaluate the model. The project includes backend processing for pose extraction and model inference, as well as a frontend for live video monitoring.
**Model Weights**: https://huggingface.co/valiantlynxz/godseye-violence-detection-model/tree/main
**Dataset**: https://huggingface.co/datasets/valiantlynxz/godseye-violence-detection-dataset
## Table of Contents
- [godseye](#godseye)
- [Table of Contents](#table-of-contents)
- [Overview](#overview)
- [Project Structure](#project-structure)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Running Backend](#running-backend)
- [Running Frontend](#running-frontend)
- [Model Training](#model-training)
- [Inference](#inference)
- [Open Source](#open-source)
- [Contributing](#contributing)
- [License](#license)
## Overview
Our system identifies violent actions in videos by using pose keypoints extracted with **YOLO**. These keypoints are then passed to an **LSTM** model that predicts whether the actions in the video are violent or non-violent.
### Project Structure
```
/godseye
│
├── /apps
│ ├── /backend
│ │ ├── /dataset_processing # Scripts to preprocess datasets and extract keypoints
│ │ ├── /model_training # Training scripts using TensorFlow
│ │ ├── /models # Trained models and checkpoints
| | |__ live_inference.py # Inference scripts for real-time video feed processing
│ │ ├── requirements.txt # Backend dependencies
│ │
│ ├── /frontend
│ ├── /static # Static files for UI
│ ├── /templates # HTML/CSS for UI/UX
│ ├── /live_feed # Frontend logic for live streaming and monitoring
│ ├── package.json # Frontend dependencies (if using Node.js, React, etc.)
│
├── /docs # Documentation
│ └── README.md
│
├── README.md # Project overview and instructions
├── LICENSE # MIT License
└── .gitignore
```
## Dataset
We use two datasets for this project:
- **RWF-2000**: A surveillance footage dataset with 2000 videos.
- **Hockey Fight**: A sports video dataset containing 1000 clips of fighting and non-fighting.
We also preprocess these videos to create a new dataset with pose keypoints.
## Installation
To install dependencies for both backend and frontend, follow these steps:
```bash
# Clone the repository
git clone https://github.com/valiantlynx/godseye.git
cd godseye
# Backend dependencies
cd apps/backend
## start environment use your favorite ide, conda, etc. otherwise use python venv
python -m venv .venv
source .venv/bin/activate # if windows .\.venv\Scripts\acticate
pip install -r requirements.txt
# Frontend dependencies (if using a framework like React)
cd apps/frontend
npm install
```
## Usage
### Running Backend
1. **Dataset Preparation**: Extract keypoints from the dataset using:
```bash
python backend/dataset_processing/extract_keypoints.py
```
2. **Model Training**: Train the LSTM model using:
```bash
python backend/model_training/train.py
```
3. **Inference**: To run inference on a live video stream:
```bash
python backend/inference/live_inference.py
```
### Running Frontend
To start the frontend for live feed monitoring:
```bash
cd apps/frontend
npm start
```
## Model Training
The LSTM model is trained on pose keypoints extracted from the videos. You can modify the training script in the `/apps/backend/model_training` directory to tune hyperparameters and model architecture.
## Inference
Inference processes pose keypoints in real-time, feeding them to the trained LSTM model to predict violent or non-violent behavior.
## Open Source
- The trained model will be uploaded to **Hugging Face**.
- The processed dataset will be shared on **Kaggle**.
## Contributing
Feel free to fork this repository, submit issues, or send pull requests for improvements.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.
git clone ssh://git@tools.uia.no:7999/ikt213g24h/godseye.git
model and dataset: https://huggingface.co/valiantlynxz/convlstm_model
clone it to the root of this project