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| title: Smart Front View Driving Assistant | |
| emoji: 🚗 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.30.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # 🚗 Smart Front-View Driving Assistant | |
| ### AI-Powered Dashcam Analysis using YOLOv8 + IoU Tracking + Risk Assessment | |
| **Developed by:** Abdulaziz Alraddai | |
| --- | |
| ## 📌 Overview | نبذة عن المشروع | |
| **Smart Front-View Driving Assistant** is an end-to-end Computer Vision & Deep Learning solution designed to assist drivers and autonomous systems by analyzing dashcam footage in real time. | |
| The system performs object detection, unique object tracking across frames, and dynamic risk assessment to identify potential hazards on the road. | |
| نظام **مساعد القيادة الذكية ذو الرؤية الأمامية** هو حل متكامل يعتمد على الذكاء الاصطناعي ورؤية الكمبيوتر لتحليل فيديوهات كاميرا القيادة (Dashcam). يقوم النظام بكشف العناصر المرورية، تتبعها بفرادة عبر الإطارات، وتقييم مستويات الخطر تلقائياً للتنبيه بالمخاطر المحتملة. | |
| --- | |
| ## ✨ Key Features | الميزات الرئيسية | |
| - **🚀 Real-Time Object Detection:** Powered by **YOLOv8** (`yolov8n`, `yolov8s`, `yolov8m`) to detect key driving objects (Cars, Pedestrians, Trucks, Buses, Traffic Lights, etc.). | |
| - **🎯 Robust Multi-Object Tracking:** Uses an **IoU (Intersection over Union) Tracker** to assign unique ID tracks to detected objects across frames. | |
| - **⚠️ Dynamic Risk Assessment:** Evaluates collision risk into 3 levels: | |
| - 🟢 **SAFE:** Normal object distance or out-of-lane. | |
| - 🟡 **CAUTION:** Object in driving corridor or approaching closer. | |
| - 🔴 **DANGER:** Critical object directly in-lane with a high frame area ratio (> 7.5%). | |
| - **📹 Video Export & Analytics:** Exports annotated H.264 MP4 videos and detailed CSV event logs with precise timestamps. | |
| - **💻 Interactive Gradio UI:** User-friendly web interface hosted on Hugging Face Spaces. | |
| --- | |
| ## 🛠️ Tech Stack | التقنيات المستخدمة | |
| - **Framework:** [Gradio](https://gradio.app/) | |
| - **Object Detection:** [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) | |
| - **Computer Vision:** OpenCV (`cv2`), ImageIO | |
| - **Data Processing:** Pandas, NumPy | |
| - **Language:** Python 3.10+ | |
| --- | |
| ## ⚙️ How It Works | آلية العمل | |
| 1. **Video Upload:** The user uploads a dashcam video (MP4/AVI/MOV). | |
| 2. **Detection & Filtering:** YOLOv8 extracts bounding boxes for driving-relevant COCO classes. | |
| 3. **IoU Association:** Objects are tracked over time using spatial overlap metrics. | |
| 4. **Risk Logic:** Calculates position relative to the central driving corridor and object bounding box area. | |
| 5. **Output Generation:** Rendered annotated video and CSV report are generated for immediate download. | |
| --- | |
| ## 🚀 Local Installation & Setup | التشغيل المحلي | |
| To run this project locally, follow these steps: | |
| ```bash | |
| # 1. Clone the repository | |
| git clone [https://huggingface.co/spaces/Aziz-AI98/driving-assistant-yolo](https://huggingface.co/spaces/Aziz-AI98/driving-assistant-yolo) | |
| cd driving-assistant-yolo | |
| # 2. Install dependencies | |
| pip install -r requirements.txt | |
| # 3. Launch the Gradio app | |
| python app.py |