BesirVelioglu/id-card-detection-rd-report

ID Card Detection, Tracking & Re-Identification — R&D Report & Prototype

Overview

This repository contains a comprehensive R&D investigation into the best modern approaches for real-time ID card detection, tracking, re-identification, and reference matching from mobile/video input, plus a working prototype implementation.

Contents

• —`RD_REPORT.md` — Full R&D report (50+ pages) covering:
• —Current system critique and limitations
• —Latest research findings (30+ papers, 2022-2026)
• —Dataset evaluation table
• —3 architecture options (Edge / Production / Research-grade)
• —Final architecture recommendation
• —Experiment design and metrics
• —Synthetic data generation pipeline
• —Training and fine-tuning recipes
• —Implementation roadmap

• —`enhanced/` — Prototype implementation:
• —config.py — Full configuration system with Edge/Production/Research presets
• —detector.py — YOLOv11 / D-FINE / RT-DETR detector with mock fallback
• —tracker.py — OC-SORT multi-object tracker (replaces ByteTrack)
• —feature_extractor.py — DINOv2 embedding extractor
• —reid_manager.py — EMA gallery for card re-entry detection
• —reference_matcher.py — SuperPoint + LightGlue reference matching
• —quality_scorer.py — Card quality assessment
• —pipeline.py — End-to-end pipeline orchestration

• —`scripts/` — Evaluation and data generation:
• —evaluate_detection.py — mAP, precision/recall evaluation
• —evaluate_tracking.py — MOTA, IDF1, ID-switch metrics
• —evaluate_reid.py — ROC/AUC, CMC, EER evaluation
• —generate_synthetic_cards.py — Privacy-safe synthetic ID card generator

• —`tests/` — Integration tests:
• —test_system.py — 68 tests covering all components

Recommended Architecture (Option B: Balanced Production)

Quick Start

from enhanced.config import PipelineConfig
from enhanced.pipeline import IDCardPipeline

config = PipelineConfig.production_config()
pipeline = IDCardPipeline(config)

# Process a frame
result = pipeline.process_frame(frame)
for card in result.cards:
    print(f"Card {card.track_id}: {card.quality_label} (conf={card.confidence:.2f})")

Tests

pip install opencv-python-headless numpy faker
python tests/test_system.py
# Expected: 68/68 passed

Key Research References

1. 1.D-FINE (arxiv:2410.13842) — Detection
2. 2.OC-SORT (arxiv:2203.14360) — Tracking
3. 3.BoT-SORT (arxiv:2206.14651) — ReID Tracking
4. 4.DINOv2 (arxiv:2304.07193) — Instance Embeddings
5. 5.LightGlue (arxiv:2306.13643) — Feature Matching
6. 6.SAM2MOT (arxiv:2504.04519) — Research-grade Tracking
7. 7.DocXPand (arxiv:2407.20662) — Synthetic Data

License

Research prototype — see individual model licenses.