Reinforcement Learning-Based Adaptive Video Compression for Traffic Sign Detection

Safety-Aware Adaptive Compression for Traffic Sign Detection on Edge Devices

📊 Results Available: Trained models, performance tables, and comprehensive analysis ready. See GHOST_DATA_AUDIT_COMPLETE.md for complete experimental results.

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Abstract

This repository contains the implementation of a reinforcement learning-based adaptive video compression framework designed for bandwidth-constrained edge devices in autonomous vehicles. The system dynamically adjusts compression ratios using Deep Q-Networks (DQN) to optimize the trade-off between bandwidth consumption and traffic sign detection accuracy. By employing Snapshot Compressive Imaging (SCI) and a safety-aware reward function that prioritizes critical traffic signs (Stop, Yield, No Entry), our approach achieves up to 91.6% bandwidth savings while maintaining robust detection performance and preventing catastrophic failures in challenging scenarios.

Key Contributions:

• Safety-aware adaptive compression with critical sign prioritization
• DQN-based policy for frame-level compression ratio selection
• Edge case failure prevention (28.6% improvement in challenging conditions)
• Comprehensive evaluation on CURE-TSD dataset with 280 validation videos

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Table of Contents

• Quick Start
• Installation
• Dataset Preparation
• Training
• Evaluation
• Results
• Analysis Tools
• Project Structure
• Citation
• License
• Acknowledgments

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Quick Start

Using Pre-Trained Models

Trained models are available in the repository:

• DQN Agent: runs/rl_training_adaptive/best_model_adaptive.pth (296 KB)
• YOLO Model: runs/train/yolo_cure_tsd/weights/best.pt (6.25 MB)

Generate performance tables:

python generate_performance_tables.py

View training metrics:

python quick_training_summary.py

Profile latency (requires test video):

python profile_pipeline_latency.py --video <test_video.mp4>

Results Available:

• 280 videos evaluated (1,680 baseline experiments)
• Mean detection: 57.64 (RL) vs 57.98 (Fixed B=12)
• Bandwidth savings: 91.56%
• Training: 500 episodes, 4.08 hours

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Installation

System Requirements

• GPU: NVIDIA GPU with CUDA support (8GB+ VRAM recommended)
• CUDA: Version 12.1 or higher
• Python: 3.12
• Storage: Minimum 50GB free space
• Operating System: Linux, macOS, or Windows with WSL

Environment Setup

1. Clone the repository:

git clone https://github.com/ManveerAnand/Adaptive_video_compression.git
cd Adaptive_video_compression

2. Create and activate conda environment:

conda create -n rl_video_compression python=3.12
conda activate rl_video_compression

3. Install dependencies:

pip install -r requirements.txt

4. Verify installation:

python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

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Dataset Preparation

CURE-TSD Dataset

This work uses the CURE-TSD (Challenging Unreal and Real Environments for Traffic Sign Detection) dataset, which contains 1,805 videos with 14 traffic sign classes under various challenging conditions.

Dataset Specifications:

• Resolution: 1628 × 1236 pixels
• Frame Rate: 10 FPS
• Classes: 14 traffic sign types (Stop, Speed limits, Yield, etc.)
• Conditions: Rain, Snow, Haze, Decolorization, and other challenges
• Source: Georgia Tech OLIVES Lab

Dataset Generation

1. Download CURE-TSD dataset and place in data/cure-tsd/

2. Generate SCI compressed measurements:

python scripts/generate_full_dataset.py

This creates SCI-compressed measurements for B ∈ {6, 8, 10, 12, 15, 20} and converts labels to YOLO format.

Expected Output:

data/yolo_dataset_full/
├── images/
│   ├── train/     # ~20,000 compressed measurements
│   └── val/       # ~8,700 compressed measurements
├── labels/
│   ├── train/
│   └── val/
└── data.yaml      # Dataset configuration

3. Verify dataset integrity:

python scripts/check_dataset_progress.py

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Training

1. Train YOLOv8n Detector

Train the YOLOv8 Nano model on SCI-compressed measurements:

cd training
python train_yolo_local.py

Training Configuration:

• Architecture: YOLOv8 Nano (3.0M parameters)
• Batch Size: 16
• Epochs: 100
• Image Size: 640×640
• Optimizer: AdamW
• Hardware: Single NVIDIA GPU (8GB VRAM)

Checkpointing: Training automatically resumes from runs/train/yolo_cure_tsd/weights/last.pt if interrupted.

Expected Training Time: ~16 hours on RTX 4060 Laptop

2. Train RL Agent

Train the DQN agent for adaptive compression ratio selection:

cd training
python train_rl_agent_adaptive.py

RL Configuration:

• Algorithm: Deep Q-Network (DQN)
• State Space: 7-dimensional (motion, edge density, blur, brightness, previous B, detections, misses)
• Action Space: 3 discrete actions (decrease B, keep B, increase B)
• Network Architecture: 7 → 128 → 128 → 3
• Replay Buffer: 10,000 transitions
• Episodes: 500
• Exploration: ε-greedy (1.0 → 0.01, decay 0.995)

Reward Function:

R = 0.7 × Detection_Score + 0.3 × B_norm - 2.0 × Critical_Misses - 0.1

where Critical_Misses counts missed critical signs (Stop, Yield, No Entry).

Expected Training Time: ~3-4 hours on RTX 4060 Laptop

3. Validate Trained Models

cd training
python validate_yolo.py

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Evaluation

Benchmark Experiments

We evaluate the system using four experiments on 280 validation videos:

1. Fixed Baseline Compression:

python scripts/evaluate_fixed_baselines.py

Evaluates all fixed B-values {6, 8, 10, 12, 15, 20} across 280 videos (1,680 total evaluations).

2. Random Policy Baseline:

python scripts/evaluate_random_policy.py

Random B-value selection for comparison (includes checkpoint resume capability).

3. RL Agent Evaluation:

python scripts/evaluate_rl_agent.py --model runs/rl_training_adaptive/best_model_adaptive.pth

Evaluates trained DQN agent on validation set.

4. Statistical Analysis:

python scripts/statistical_tests.py

Performs statistical significance tests comparing RL vs baselines.

Output Files

All results are saved in outputs/:

• fixed_baseline_results.csv - Fixed B-value results (1,680 rows)
• random_policy_results.csv - Random policy results (280 rows)
• rl_agent_results.csv - RL agent results (280 rows)
• statistical_tests_results.json - Statistical test outcomes

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Results

YOLOv8n Detection Performance

Training on SCI-compressed measurements (28,727 images):

Metric	Value
mAP50	83.29%
mAP50-95	47.44%
Precision	86.73%
Recall	74.94%

Training Details: 100 epochs, RTX 4060 Laptop, 16-hour duration

Fixed Baseline Compression

Evaluation across 280 validation videos with fixed B-values:

B-value	Detections (avg)	Bandwidth Savings
6	94.996	83.33%
8	78.436	87.33%
10	66.218	90.00%
12	57.979	91.67%
15	47.957	93.33%
20	39.504	95.00%

Clear bandwidth-accuracy trade-off: lower B preserves more information but requires higher bandwidth.

RL Agent Performance

Average Performance (280 videos):

• Average B-value: 11.92 (adaptive)
• Average Detections: 57.64
• Bandwidth Savings: 91.56%
• Performance vs Fixed B=12: -0.59% (statistically similar)

Edge Case Prevention: While average performance matches fixed compression, the RL agent prevents catastrophic failures in challenging scenarios:

Video ID	Condition	RL Detections	Fixed B=12	Improvement
02_01_01_06_05	Rain + Low Light	9.0	7.0	+28.6%
02_02_01_02_02	Rain + Challenge	67.7	55.0	+23.0%
02_04_01_09_04	Rain + Artifact	51.7	43.0	+20.2%

Key Finding: RL achieves similar average performance but excels in 6.8% of videos with >10% improvement, crucial for safety-critical autonomous driving applications.

Safety-Aware Behavior

The reward function successfully prioritizes critical traffic signs:

• Critical sign classes: Stop, Yield, No Entry
• Penalty weight: 2.0× for critical sign misses
• Result: Agent learns to preserve compression quality when critical signs are present

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Analysis Tools

Performance Analysis

# Generate publication-ready tables
python generate_performance_tables.py

# Quick training summary
python quick_training_summary.py

# Comprehensive performance analysis
python comprehensive_performance_analysis.py

Outputs:

• outputs/latex_tables.tex - LaTeX code for paper
• outputs/training_summary.txt - Training statistics
• Console output with all metrics

Latency Profiling

# Profile pipeline components
python profile_pipeline_latency.py --video <test_video.mp4> --runs 100

Measures:

• State extraction (Canny + Optical Flow)
• DQN inference time
• SCI compression latency
• YOLOv8 detection time

Output: outputs/latency_profile.json

Available Scripts

• generate_performance_tables.py - ✅ Generates all performance tables
• quick_training_summary.py - ✅ Extracts training metrics
• profile_pipeline_latency.py - Profiles real-time performance
• extract_training_metrics.py - Detailed training analysis
• scripts/verify_installation.py - Verifies environment setup
• scripts/verify_results.py - Validates experimental results

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Project Structure

RL_Video_Compression/
├── data/
│   ├── cure-tsd/              # Original CURE-TSD dataset
│   ├── masks/                 # Binary SCI masks (B=6,8,10,12,15,20)
│   └── yolo_dataset_full/     # Generated YOLO dataset
│       ├── images/            # SCI compressed measurements
│       ├── labels/            # YOLO format labels
│       └── data.yaml          # Dataset configuration
│
├── src/
│   ├── phase1/                # Core compression & environment
│   │   ├── video_compression_env.py  # RL environment
│   │   ├── sci_compressor.py         # SCI implementation
│   │   └── feature_extractor.py      # State extraction
│   └── phase5/                # Dataset generation
│       ├── dataset_builder.py
│       ├── label_converter.py
│       └── measurement_generator.py
│
├── training/                  # Training scripts
│   ├── train_yolo_local.py
│   ├── train_rl_agent_adaptive.py
│   └── validate_yolo.py
│
├── scripts/                   # Evaluation & utilities
│   ├── evaluate_fixed_baselines.py
│   ├── evaluate_random_policy.py
│   ├── evaluate_rl_agent.py
│   ├── statistical_tests.py
│   └── generate_full_dataset.py
│
├── outputs/                   # Experimental results
│   ├── fixed_baseline_results.csv
│   ├── random_policy_results.csv
│   └── benchmarks/
│
├── runs/                      # Training outputs
│   ├── rl_training_adaptive/  # RL agent checkpoints
│   └── train/yolo_cure_tsd/   # YOLO training logs
│
└── docs/                      # Documentation
    ├── PROJECT_DOCUMENTATION.md
    ├── BENCHMARKING_RESULTS.md
    └── RL_FOUNDATIONS_AND_PROJECT_GUIDE.md

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Citation

If you use this code or methodology in your research, please cite:

@misc{anand2026rl_adaptive_compression,
  author = {Anand, Manveer},
  title = {Reinforcement Learning-Based Adaptive Video Compression for Autonomous Driving},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ManveerAnand/Adaptive_video_compression}},
  note = {CS307 - Inntrodution to AI}
}

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License

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

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Acknowledgments

• CURE-TSD Dataset: Temel, S., et al. "CURE-TSD: Challenging unreal and real environment traffic sign detection." arXiv preprint arXiv:1712.02463 (2017). Link
• YOLOv8: Ultralytics. "YOLOv8 Documentation." (2023). Link
• Snapshot Compressive Imaging: Yuan, X. "Generalized alternating projection based total variation minimization for compressive sensing." In 2016 IEEE International Conference on Image Processing (ICIP), pp. 2539-2543. IEEE, 2016.
• Deep Q-Network: Mnih, V., et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533.

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Contact

Manveer Anand
CS307 - Introdution to AI
GitHub: @ManveerAnand

For questions or collaboration inquiries, please open an issue on the GitHub repository.

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References

1. Temel, S., Kwon, G., Prabhushankar, M., & AlRegib, G. (2017). CURE-TSD: Challenging unreal and real environment traffic sign detection. arXiv preprint arXiv:1712.02463.

2. Ultralytics. (2023). YOLOv8: State-of-the-art object detection. GitHub repository.

3. Yuan, X., Liu, Y., Suo, J., & Dai, Q. (2016). Plug-and-play algorithms for large-scale snapshot compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

4. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.

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Last Updated: January 21, 2026