NPP-People-Tracking

A multi-object tracking framework featuring YOLOv8 detection, DeepSORT-based association, and three evolutionary optimizers (QPSO, NSGA-II, MOPSO). Designed for robust benchmarking, automatic tuning, and performance visualization across multiple feature extractors and tracking configurations.

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🚀 Features

• Track-by-detection framework using YOLOv8 + DeepSORT

• GPU-accelerated inference via PyTorch & Ultralytics

• Three evolutionary optimizers:

  • QPSO (Quantized Particle Swarm Optimization)
  • NSGA-II (Non-dominated Sorting Genetic Algorithm)
  • MOPSO (Multi-objective PSO)

• Three feature extractors (embedders):

  • MobileNetV2
  • ShuffleNetV2
  • ResNet

• Automatic batch optimization across all 3×3 (embedder × optimizer) combinations

• Mock MOTA, IDF1, and FPS evaluation for fast prototyping

• Pareto front visualizations (2D and 3D)

• Modular codebase for easy extension & full GPU compatibility

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📁 Folder Structure

NPP-People-Tracking/
├── detectors/               # YOLOv8 detection wrapper
├── embeddings/              # Feature extractors
├── evaluation/              # Evaluation logic
├── optimization/            # QPSO, NSGA, MOPSO + plotting
├── results/                 # Logs, metrics, plots
├── sample_videos/          # Input test videos
├── trackers/                # DeepSORT / ByteTrack wrappers
├── utils/                   # Drawing and utility helpers
├── main.py                  # Visual demo runner
├── batch_runner.py          # Full automation across all configs
├── run_plot_pareto.py       # Manual plotting script
├── config.yaml              # Tuning bounds for all optimizers

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⚙️ Setup

1. Install Dependencies

pip install -r requirements.txt

2. Download YOLOv8 Weights

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt

Place it in the root directory as yolov8n.pt

3. Add a test video

Place your input video in sample_videos/test_video.mp4

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🧪 Running the System

🔁 Run Full Batch (All 3×3 Combinations)

python batch_runner.py

This will:

• Run QPSO, NSGA-II, and MOPSO for MobileNetV2, ShuffleNetV2, and ResNet
• Save logs, metrics, plots
• Output a combined summary CSV at results/summary_table.csv

▶️ Run Demo Tracker

python main.py

Visualize tracking with live FPS overlay and bounding boxes.

📊 Generate Plots Manually

python run_plot_pareto.py

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📈 Output Artifacts

• metrics.json: Best config + result for each combo
• log.csv: All 50 generations of evaluations
• *_2d.png, *_3d.png: Saved Pareto plots (FPS vs MOTA, FPS vs MOTA vs IDF1)
• summary_table.csv: Global table across all runs

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🧠 Technical Notes

• All tracking logic and evaluation runs on GPU by default
• Mock metrics are used for rapid testing, customizable in evaluation.py
• Optimizers operate over bounds defined in optimization/config.yaml
• Supports future upgrades for real GT-based evaluation using py-motmetrics

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🤝 Contributors

• Abhinav Shukla Project design, optimization logic, performance benchmarking