{"id":25534601,"url":"https://github.com/aksweb/rjpolice_hack_472_cyberknights_6","last_synced_at":"2025-06-28T22:37:37.547Z","repository":{"id":219894222,"uuid":"733602631","full_name":"aksweb/RJPOLICE_HACK_472_CyberKnights_6","owner":"aksweb","description":"System for Geo-Tagging of privately owned cameras.","archived":false,"fork":false,"pushed_at":"2024-03-05T17:30:23.000Z","size":71413,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-29T18:10:21.446Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://www.youtube.com/watch?v=sDWuOFIDW-I","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RJPOLICE_HACK_472_CyberKnights_6\n# Project Name: Nazar\n# [Video Demonstration _(Click Here)_](https://www.youtube.com/watch?v=sDWuOFIDW-I) \n## Flow\n![Control Dashboard](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ml/flow.png)\n## Front-end Screenshot\n![Control Dashboard](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ongoing_frontend/screenshots/1.png)\n![Control Dashboard](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ongoing_frontend/screenshots/mapalert.png)\n![ALERT](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ongoing_frontend/screenshots/alert.png)\n![ANPR](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ongoing_frontend/screenshots/se.png)\n![Registration](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ongoing_frontend/screenshots/2.png)\n\n## Demo Screenshots\n![Registration](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/demo_working/screenshots/Screenshot%20(1697).png)\n![Registration](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/demo_working/screenshots/Screenshot%20(1699).png)\n\n## Features\n\n- **Object Detection:** Utilizes YOLOv5 for real-time detection of various objects and events in a given scene.\n- **Face Detection:** Using YOLOv5 for real-time face detection in a given scene.\n-  ![Face Detection Video](ml/face1.gif)\n- **Incident Identification:** Focuses on identifying and highlighting instances of violence, riots, crimes, and accidents.\n- **Real-Time Processing:** Optimized for processing video streams in real-time.\n- **ANPR mounted on vehicle** \n- **Visual Alerts:** Provides visual alerts or annotations for detected incidents.\n\n# ML Models:\n## Architecture\n![Control Dashboard](https://github.com/aksweb/RJPOLICE_HACK_472_CyberKnights_6/blob/main/ml/modelarc.png)\n## Implement image processing algorithms:\n\n### 1. Vehicle Detection with Number Plate Recognition using YOLOv5:\n\nThe YOLO (You Only Look Once) model is a real-time object detection system known for its speed and accuracy. In this project, we have trained YOLOv5 to specifically detect vehicles and recognize their number plates.\n  \n### View the results:\nDetected vehicles and their number plates will be highlighted in the output video.\n\n## 2. Face Matching using ArcFace:\n\nArcFace is a state-of-the-art face recognition model. The system captures video from a camera source, detects faces, and matches them against a database using ArcFace embeddings.\n\n## 3. Crowd Congestion Detection using CSRNet:\n\nCSRNet is a deep learning model designed for counting and density estimation in crowded scenes. The system captures video from a camera source, processes the frames using CSRNet, and estimates the crowd density, identifying congestion areas.\n\n **Visualization:** Provides a visual representation of crowd density and congestion areas.\n\n ## 4. Suspicious Object Detection using YOLOv5:\n \nYOLOv5 is a powerful object detection model. The system captures video from a camera source, processes the frames using YOLOv5, and identifies and highlights suspicious objects in real time.\n\n**Visual Alerts:** Provides visual alerts or annotations for detected suspicious objects.\n\n## 5. Detection of Violence, Riots, Crimes, and Accidents using YOLOv5:\n\nYOLOv5 is a state-of-the-art object detection model, to detect and identify instances of violence, riots, crimes, and accidents in video streams. The system captures real-time video from a camera source, processes the frames using YOLOv5, and provides alerts or annotations for the detected incidents.\n ![Face Detection Video](ml/sim2.gif)\n# HOW TO ACCESS DEMO\n# Camera Registration System\n\nThis project is a Camera Registration System developed using the MEFN stack (MongoDB, Express.js, Flask, Node.js).\n\n## Prerequisites\n\nBefore running the application locally, make sure you have the following installed:\n\n- [Node.js](https://nodejs.org/) (including npm)\n- [MongoDB](https://www.mongodb.com/try/download/community)\n\n## Getting Started\n\n1. **Clone the repository:**\n\n    ```bash\n    git clone https://github.com/your-username/camera-registration-system.git\n    cd camera-registration-system\n    ```\n\n2. **Install Dependencies:**\n\n    ```bash\n    # Install server dependencies\n    cd backend\n    npm install\n\n    # Install client dependencies\n    cd ../frontend\n    npm install\n    ```\n\n3. **Configure MongoDB:**\n\n    - Make sure MongoDB is running locally.\n    - Update the MongoDB connection string in `backend/config/database.js` if needed.\n\n4. **Run the Application:**\n\n    ```bash\n    # Start the server (from the 'backend' directory)\n    cd backend\n    node src/app.js\n\n    # Start the client (from the 'frontend' directory)\n    cd ../frontend\n    start index.html\n    ```\n\n5. **In case it doesn't open:**\n\n    - Open your browser and go to [http://localhost:3000](http://localhost:3000).\n\n\n\n## Additional Notes\n\n- The application uses Flask for the frontend, Express.js for the backend, and MongoDB for data storage.\n- Make sure to set up the Google Maps API key in the frontend (`frontend/js/script.js`) for map functionality.\n- Adjust the paths and configurations as needed for your specific setup.\n\nFeel free to contribute, report issues, or provide feedback!\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faksweb%2Frjpolice_hack_472_cyberknights_6","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faksweb%2Frjpolice_hack_472_cyberknights_6","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faksweb%2Frjpolice_hack_472_cyberknights_6/lists"}