https://github.com/aksweb/rjpolice_hack_472_cyberknights_6
System for Geo-Tagging of privately owned cameras.
https://github.com/aksweb/rjpolice_hack_472_cyberknights_6
Last synced: 4 months ago
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System for Geo-Tagging of privately owned cameras.
- Host: GitHub
- URL: https://github.com/aksweb/rjpolice_hack_472_cyberknights_6
- Owner: aksweb
- Created: 2023-12-19T17:41:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-05T17:30:23.000Z (over 1 year ago)
- Last Synced: 2024-03-06T17:43:11.950Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage: https://www.youtube.com/watch?v=sDWuOFIDW-I
- Size: 68.1 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RJPOLICE_HACK_472_CyberKnights_6
# Project Name: Nazar
# [Video Demonstration _(Click Here)_](https://www.youtube.com/watch?v=sDWuOFIDW-I)
## Flow
## Front-end Screenshot
## Demo Screenshots
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## Features
- **Object Detection:** Utilizes YOLOv5 for real-time detection of various objects and events in a given scene.
- **Face Detection:** Using YOLOv5 for real-time face detection in a given scene.
- 
- **Incident Identification:** Focuses on identifying and highlighting instances of violence, riots, crimes, and accidents.
- **Real-Time Processing:** Optimized for processing video streams in real-time.
- **ANPR mounted on vehicle**
- **Visual Alerts:** Provides visual alerts or annotations for detected incidents.
# ML Models:
## Architecture
## Implement image processing algorithms:
### 1. Vehicle Detection with Number Plate Recognition using YOLOv5:
The 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.
### View the results:
Detected vehicles and their number plates will be highlighted in the output video.
## 2. Face Matching using ArcFace:
ArcFace 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.
## 3. Crowd Congestion Detection using CSRNet:
CSRNet 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.
**Visualization:** Provides a visual representation of crowd density and congestion areas.
## 4. Suspicious Object Detection using YOLOv5:
YOLOv5 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.
**Visual Alerts:** Provides visual alerts or annotations for detected suspicious objects.
## 5. Detection of Violence, Riots, Crimes, and Accidents using YOLOv5:
YOLOv5 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.
# HOW TO ACCESS DEMO
# Camera Registration System
This project is a Camera Registration System developed using the MEFN stack (MongoDB, Express.js, Flask, Node.js).
## Prerequisites
Before running the application locally, make sure you have the following installed:
- [Node.js](https://nodejs.org/) (including npm)
- [MongoDB](https://www.mongodb.com/try/download/community)
## Getting Started
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/camera-registration-system.git
cd camera-registration-system
```
2. **Install Dependencies:**
```bash
# Install server dependencies
cd backend
npm install
# Install client dependencies
cd ../frontend
npm install
```
3. **Configure MongoDB:**
- Make sure MongoDB is running locally.
- Update the MongoDB connection string in `backend/config/database.js` if needed.
4. **Run the Application:**
```bash
# Start the server (from the 'backend' directory)
cd backend
node src/app.js
# Start the client (from the 'frontend' directory)
cd ../frontend
start index.html
```
5. **In case it doesn't open:**
- Open your browser and go to [http://localhost:3000](http://localhost:3000).
## Additional Notes
- The application uses Flask for the frontend, Express.js for the backend, and MongoDB for data storage.
- Make sure to set up the Google Maps API key in the frontend (`frontend/js/script.js`) for map functionality.
- Adjust the paths and configurations as needed for your specific setup.
Feel free to contribute, report issues, or provide feedback!