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| title: Driver Drowsiness Detection | |
| emoji: 🚗 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.31.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| paper: arxiv.org/abs/2505.17392 | |
| # Driver Drowsiness Detection System | |
| This is a real-time driver drowsiness detection system that uses computer vision and deep learning to detect signs of drowsiness in drivers. The system can process webcam feeds, video files, and single images. | |
| ## Features | |
| - Real-time webcam monitoring | |
| - Video file processing | |
| - Single image analysis | |
| - Face detection and drowsiness prediction | |
| - Visual feedback with bounding boxes and status indicators | |
| ## How to Use | |
| 1. **Webcam Mode**: Click the "Start Webcam" button to begin real-time monitoring | |
| 2. **Video Mode**: Upload a video file for processing | |
| 3. **Image Mode**: Upload a single image for analysis | |
| The system will display the results with: | |
| - Green box: Alert (not drowsy) | |
| - Red box: Drowsy | |
| - Probability score for drowsiness | |
| ## Technical Details | |
| - Built with PyTorch and Vision Transformer (ViT) | |
| - Uses OpenCV for face detection | |
| - Gradio interface for easy interaction | |
| - Real-time processing capabilities | |
| ## Model | |
| The system uses a Vision Transformer (ViT) model trained on driver drowsiness detection. The model is capable of detecting subtle signs of drowsiness in facial expressions. |