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This system analyzes vehicle counts from video feeds, processes the data using machine learning models, and adjusts traffic signal timings to improve traffic flow.\n\n## 📸 Screenshots\n\n![1](screenshots/1.png)\u003cbr/\u003e\u003cbr/\u003e\n![2](screenshots/2.png)\u003cbr/\u003e\u003cbr/\u003e\n![3](screenshots/3.png)\n\n\n## ✨ Features\n- Vehicle Detection: Uses YOLOv4 for real-time vehicle detection from video feeds.\n- Traffic Optimization: Employs a genetic algorithm to determine optimal green light times based on vehicle counts.\n- Web Interface: Allows users to upload traffic videos, view processing results, and receive optimized traffic management recommendations.\n\n## 🚀 Getting Started\n\n### Prerequisites\n\n- Python 3.x\n- Nodejs\n- OpenCV\n- YOLOv4 weights and configuration files\n- Required Python packages (listed in requirements.txt)\n\n## 💻 Local Setup\n\nClone the repository:\n\n```bash\ngit clone https://github.com/ashish0kumar/AI-Based-Traffic-Management.git\ncd AI-Based-Traffic-Management\n```\n\nStart the backend server:\n\n```bash\ncd backend\npip install -r requirements.txt\npython app.py\n```\n\nStart the frontend server:\n```bash\ncd frontend\nnpm install\nnpm start\n```\n\nUpload Traffic Videos: \u003cbr/\u003e\nUse the web interface to upload 4 traffic videos. The system will process the videos and display optimized green light times based on the analysis.\n\n## 🙏 Acknowledgments\n\n- YOLOv4: For vehicle detection.\n- OpenCV: For video processing.\n- Genetic Algorithm: For optimizing traffic light timings.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashish0kumar%2Fai-based-traffic-management-sih","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashish0kumar%2Fai-based-traffic-management-sih","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashish0kumar%2Fai-based-traffic-management-sih/lists"}