cvtechniques/DriftVision

🏎 Drift Car Tracking & Zone Analysis Model

📌 Overview

This project is a computer vision model designed to track drifting cars and quantify driver performance using aerial (drone) footage. The system detects and tracks vehicles during tandem runs and measures how they interact with predefined drift zones.

The current implementation is a proof of concept, developed specifically for footage from Evergreen Speedway in Monroe, Washington.

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🧠 Model Description

This model uses a YOLO-based framework to:

• Detect drift cars in tandem runs
• Classify vehicles as:
  • leader
  • chaser
• Classify zones as:
  • FrontZone
  • RearZone
• Track vehicles across frames
• Enable downstream analysis of zone interaction and timing

Training Details

• Fine-tuned from a pretrained YOLO model
• Custom dataset manually annotated
• Two datasets:
  • Cars: Bounding boxes for leader and chaser
  • Zones: Segmentation masks for drift zones

Zone interaction is computed using geometric methods (polygon overlap + time tracking), not learned directly by the model.

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🎯 Intended Use

Designed for:

• Formula Drift-style competitions
• Grassroots drifting events
• Experimental motorsports analytics

Example Applications

• Measuring time spent in drift zones
• Analyzing tandem behavior (leader vs. chaser)
• Supporting judging with quantitative insights
• Enhancing broadcast overlays

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📊 Training Data

📁 Source

• Formula Drift Seattle 2025 PRO, Round 6 - Top 32
  https://www.youtube.com/watch?v=MuD-uxGQnrg&t=879s

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🔢 Dataset Size

🚗 Cars

• 1,204 original → 2,724 augmented
• Split: 84% train / 12% val / 5% test

🟣 Zones

• 724 original → 1,666 augmented
• Split: 85% train / 9% val / 6% test

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🏷 Class Distribution

Class	Count
Leader	1,204
Chaser	1,201
FrontZone	137
RearZone	588

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✏️ Annotation

• Fully manual annotation
• Consistent labeling across frames
• Handled occlusion, overlap, and tandem proximity

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🔧 Augmentation

• Rotation ± 8°
• Saturation ± 15%
• Brightness ± 10%
• Blur 2px
• Mosaic = 0.2
• Scale ± 15%
• Translate ± 5%
• Hsv_h (Color tint) = 0.01

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⚙️ Training Procedure

• Framework: Ultralytics YOLO
• Models:
  • Cars: YOLO26s
  • Zones: YOLO26s-seg

💻 Hardware

• NVIDIA A100 (Google Colab)

⏱ Training Time

• Cars: 80 epochs (~42 min)
• Zones: 140 epochs (~1h 14min)

⚙️ Settings

• Batch size: 16
• Image size: 1024
• Workers: 8
• Cls: 2.5 (Only for Object Detection)
• No early stopping
• Default preprocessing

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📈 Evaluation Results

🚗 Car Model

Metric	Value
Precision	0.9904
Recall	0.9792
mAP@50	0.9882
mAP@50-95	0.8937

🟣 Zone Model

Metric	Value
Precision	0.9919
Recall	0.9952
mAP@50	0.9948
mAP@50-95	0.7064

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📉 Key Visualizations

Car Results Zone Results

🧠 Performance Analysis

🚗 Cars

Strengths: - Very high precision and recall
- Reliable detection and classification
- Strong tracking foundation
Limitations: - Smoke occlusion affects detection
- Close tandem overlap can cause confusion
- Limited generalization beyond training conditions

🟣 Zones

• High detection accuracy (mAP@50)
• Lower boundary precision (mAP@50-95) Implication:
• Good at identifying zones
• Less accurate for exact boundaries → impacts timing precision
  Note: Since zones are static, polygon-based methods may be more reliable than segmentation.

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⚠️ Limitations and Biases

🚨 Failure Cases

• Heavy smoke → missed or unstable detections
• Close tandem → overlap confusion
• Camera motion → inconsistent zone alignment
• Edge-of-frame → partial detections

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📉 Weak Areas

• Zone boundary precision
• Leader vs. chaser ambiguity in tight proximity

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📊 Data Bias

• Single track (Evergreen Speedway)
• Single event and lighting condition
• Fixed drone perspective

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🌦 Environmental Limits

Performance may degrade with: - Smoke, blur, or occlusion
- Lighting changes
- Drone altitude variation
- Camera movement

🚫 Not Suitable For

• Official judging systems
• General vehicle detection
• Different tracks without recalibration
• Other motorsports without adaptation

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📏 Dataset Limitations

• Underrepresented zone classes
• Limited diversity (track, cars, conditions)
• Few edge-case scenarios (spins, collisions)

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🏁 Summary

This model performs strongly within a controlled environment but is highly specialized. It should be viewed as a proof-of-concept system for drift analytics rather than a fully generalized or production-ready solution.