YOLOv9 and DeepSORT for Real-Time Object Tracking in the CARLA Simulator

Project Overview

This project integrates the powerful YOLOv9 object detection algorithm with DeepSORT for real-time multi-object tracking within the CARLA Simulator, a leading platform for autonomous vehicle research. The solution is designed to detect and track objects in dynamic environments, enabling advanced perception and trajectory planning.

Key Features:

• Real-time object detection and tracking using YOLOv9 and DeepSORT.
• Seamless integration with the CARLA Simulator.
• Highly adaptable for autonomous driving research and applications.

Core Technologies:

• YOLOv9: Advanced object detection.
• DeepSORT: Robust real-time tracking.
• CARLA Simulator: Autonomous driving research simulation.
• OpenCV: Image processing.
• PyTorch: Deep learning framework.
• NumPy: Numerical computations.

carla.mp4

---

Installation

Prerequisites:

• Python 3.7 or higher.
• CARLA Simulator 0.9.11.

Installation Steps:

1. Download and Set Up CARLA Simulator:

   • Download CARLA 0.9.11 from the CARLA releases page.
   • Extract the ZIP file and follow the CARLA official documentation for installation.

   cd CARLA_0.9.11

2. Clone This Repository:

   git clone https://github.com/ROBERT-ADDO-ASANTE-DARKO/YOLOv9-DeepSORT-realtime-object-tracking-CARLA.git
   cd YOLOv9-DeepSORT-realtime-object-tracking-CARLA

3. Install Dependencies: Install all required Python libraries:

   pip install -r requirements.txt

---

Usage Instructions

Step 1: Trajectory Planning in CARLA

1. Copy the trajectory planning script:

   cp trajectory_planning.py CARLA_0.9.11/PythonAPI/examples/

2. Launch the CARLA Simulator:

   cd CARLA_0.9.11
   ./CarlaUE4.exe
   ./CarlaUE4 -dx11

3. Run the trajectory planning script: Open a new terminal, navigate to the CARLA PythonAPI examples directory, and execute the script:

   cd CARLA_0.9.11/PythonAPI/examples
   python trajectory_planning.py

Step 2: YOLOv9 and DeepSORT Object Tracking

1. Clone the YOLOv9 Repository:

   git clone https://github.com/WongKinYiu/yolov9.git
   cd yolov9

2. Prepare the Recorded Video:

   • Copy the video recorded in CARLA to the YOLOv9 directory.

3. Add the Tracking Script:

   • Copy detect_dual_tracking.py to the YOLOv9 directory.

4. Run the Tracking Script: Execute the following command to detect and track objects in the video:

   python detect_dual_tracking.py --weights 'yolov9-c.pt' --source '/yolov9/video.mp4' --device 'cpu'

---

Results and Visualizations

This project outputs:

• A video with detected and tracked objects.
• Trajectories and positional data for further analysis.

---

Contributing

Contributions are welcome! If you'd like to improve this project:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

---

License

This project is licensed under the MIT License.