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CCTV Monitoring with YOLOv8

This project is a sophisticated CCTV monitoring application designed to detect and count objects such as cars, motorcycles, and people using the cutting-edge YOLOv8 model. The application supports multiple CCTV camera feeds via RTSP, making it ideal for real-time monitoring and analysis in urban environments, traffic management, and security systems.

Features

• Real-Time Object Detection: Leverages YOLOv8 for quick and precise object detection.

• Multi-Camera Integration: Handles multiple RTSP streams, allowing concurrent monitoring from various locations.

• Object Counting: Efficiently counts objects like cars, motorcycles, and people, providing valuable data for traffic and security applications.

• User-Friendly Interface: Easy-to-use interface for configuring detection parameters and viewing results in real-time.

• GPU Acceleration: Optimized for high performance using GPU, ensuring smooth processing of high-resolution video streams.

• Scalability: Suitable for large-scale deployments with the ability to handle multiple camera feeds.

Installation

To set up the application, follow these instructions:

1. Clone the repository:

   git clone https://github.com/Arifmaulanaazis/CCTV-Monitoring.git
   cd CCTV-Monitoring

2. Install dependencies:

   Ensure you have Python 3.8+ and a CUDA-capable GPU. Then, install the required packages:

   pip install -r requirements.txt

3. Configure RTSP streams:

   You will need to create and edit the urls.json file to specify your camera sources. This file should contain location names as keys and a list of camera URLs or indices as values. Here's an example format:

   {
       "Location 1": [
           "rtsp://username:password@ip_address:port/path"
       ],
       "Location 2": [
           "http://streaming_address:port/path",
           "1"  // This can be a local camera index (e.g., 0, 1, 2) for USB webcams
       ],
       ...
   }

   Note: The urls.json file can contain various camera sources, such as:

   • RTSP URLs: For IP cameras.
   • HTTP/HTTPS Streaming URLs: For web streams.
   • Local Camera Indices: Using numbers like 0, 1, 2, etc., to reference built-in or USB webcams connected to your machine.

4. Run the application:

   Start the application with the following command:

   python GUI.py

Usage

• Monitoring: View live detection results and object counts directly through the application's interface.

• Data Analysis: Export detection data for further analysis or integration with other systems.

Applications

• Traffic Monitoring: Analyze traffic patterns, vehicle counts, and congestion levels in real-time.

• Security Surveillance: Enhance security by detecting and counting people and vehicles in restricted areas.

• Urban Planning: Gather valuable data for city planning and infrastructure development.

Contributions

Contributions are welcome! Please feel free to submit issues, pull requests, or suggest enhancements.

License

This project is licensed under the MIT License.

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