Traffic Monitoring System using YOLOv4 and DeepSORT

This project implements a traffic monitoring system using YOLOv4 for object detection and DeepSORT for object tracking. The system is designed to detect and track vehicles in real-time video streams, allowing for various applications such as traffic analysis, vehicle counting, and monitoring.

Features

• Real-time vehicle detection using YOLOv4
• Object tracking using DeepSORT algorithm
• Support for multiple cameras or video streams
• Customizable configurations for detection and tracking parameters
• Visualization of detected and tracked vehicles on video output

Installation

Requirements

• Python 3.x
• CUDA-enabled GPU (optional, for GPU acceleration)
• OpenCV
• TensorFlow or PyTorch (for YOLOv4)
• DeepSORT

Setup

1. Clone this repository:git clone https://github.com/AnandAnnapur/Traffic-Monitoring-Object-Tracking-And-Counting--Using-YOLO-And-deepSORT.git

2. Install dependencies:pip install -r requirements.txt

3. Download YOLOv4 weights and configuration file from official repository or YOLO website.

4. Download DeepSORT model weights and configuration from the DeepSORT repository or official site.

5. Place the YOLOv4 and DeepSORT configuration files and weights in the appropriate directories within the project.

Usage

1. Run the trafficMonitor.py script:python trafficMonitor.py.

2. Adjust detection and tracking parameters as needed in the configuration files.

3. Monitor the output video stream for detected and tracked vehicles.

Contributors

• Amith M Jain
• Anand Annapur G V
• Srushti Mahesh