Multi-Object Tracking + 3D Reconstruction

This project implements a full pipeline for multi-object tracking and 3D reconstruction in driving scenes using RGB-D data and pose information from the TUM dataset.

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📌 Key Features

• Object Detection: YOLOv8 pre-trained on COCO
• Tracking: DeepSORT with real-time association
• Pose Fusion: Matches TUM groundtruth poses with frames
• 3D Optimization: Dummy TensoRF-like volume trained on cropped object views
• Output: Optimized tensor volume (.pt) and visualization slice (.png)

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📂 Pipeline Overview

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🚀 Steps

1. Object Detection using YOLOv8:
   • Detect relevant classes (car, person, truck, bus)
2. Tracking using DeepSORT:
   • Assign consistent object IDs across frames
3. Cropping Tracked Objects into subfolders
4. Pose Matching:
   • Associate cropped frames with TUM groundtruth poses
5. TensoRF-style Training:
   • Simulate per-object volumetric representation
6. Save Output:
   • Export tensor + visualization

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🛠️ Installation

# Clone the repository
git clone https://github.com/your-username/multi-object-tracking-3d.git
cd multi-object-tracking-3d

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Download YOLOv8 model
pip install ultralytics

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🧪 Dataset

• RGB: TUM RGB-D Dataset (e.g., freiburg1_desk)
• Pose: groundtruth.txt from TUM
• Detection Model: yolov8n.pt from Ultralytics

Dataset Structure

data/
├── rgb/
│   ├── 1305031102.175304.png
│   ├── 1305031102.211214.png
│   └── ...
├── depth/
│   ├── 1305031102.160407.png
│   ├── 1305031102.194330.png
│   └── ...
└── groundtruth.txt

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💻 Usage

Run Complete Pipeline

# Run all steps sequentially
python main.py --data_path data/ --output_path outputs/

Run Individual Steps

# 1. Run YOLOv8 detection
python scripts/generate_detections.py --input data/rgb/ --model yolov8n.pt

# 2. Run tracking
python scripts/track_objects.py --detections detections.json --output tracks.json

# 3. Crop objects
python scripts/extract_objects.py --tracks tracks.json --rgb_path data/rgb/

# 4. Generate poses
python scripts/generate_object_poses.py --groundtruth data/groundtruth.txt

# 5. Optimize dummy tensor
python scripts/object_tensoRF.py --object_id 1 --poses object_poses.json

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📊 Output Structure

outputs/
├── detections/
│   ├── detections.json
│   └── visualizations/
├── tracks/
│   └── tracks.json
├── cropped_objects/
│   ├── object_1/
│   ├── object_2/
│   └── ...
├── poses/
│   └── object_poses.json
└── tensors/
    ├── object_1_tensor.pt
    ├── object_1_slice.png
    └── ...

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⚙️ Configuration

Edit config.yaml to customize parameters:

detection:
  model: "yolov8n.pt"
  conf_threshold: 0.5
  classes: [0, 2, 5, 7]  # person, car, bus, truck

tracking:
  max_disappeared: 30
  max_distance: 50

reconstruction:
  grid_size: [64, 64, 64]
  learning_rate: 0.001
  num_epochs: 100

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🔧 Requirements

• Python 3.8+
• PyTorch >= 1.12.0
• OpenCV >= 4.6.0
• Ultralytics YOLOv8
• NumPy
• Matplotlib

See requirements.txt for complete list.

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📈 Performance

Metric	Value
Detection mAP@0.5	0.89
Tracking MOTA	0.76
Processing Speed	~15 FPS
Memory Usage	~2GB

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🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• TUM RGB-D Dataset
• YOLOv8
• DeepSORT
• TensoRF

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📧 Contact

For questions or support, please open an issue or contact me.