| # 🏎 Drift Car Tracking & Zone Analysis Model |
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| ## 📌 Overview |
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| 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. |
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| The current implementation is a **proof of concept**, developed specifically for footage from **Evergreen Speedway in Monroe, Washington**. |
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| ## 🧠 Model Description |
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| This model uses a YOLO-based framework to: |
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| - 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 |
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| ### Training Details |
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| - 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 |
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| Zone interaction is computed using geometric methods (polygon overlap + time tracking), not learned directly by the model. |
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| ## 🎯 Intended Use |
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| Designed for: |
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| - Formula Drift-style competitions |
| - Grassroots drifting events |
| - Experimental motorsports analytics |
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| ### Example Applications |
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| - 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 |
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| ### 📁 Source |
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| - 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 |
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| #### 🚗 Cars |
| - 1,204 original → 2,724 augmented |
| - Split: 84% train / 12% val / 5% test |
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| #### 🟣 Zones |
| - 724 original → 1,666 augmented |
| - Split: 85% train / 9% val / 6% test |
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| ### 🏷 Class Distribution |
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| | Class | Count | |
| |-----------|------| |
| | Leader | 1,204 | |
| | Chaser | 1,201 | |
| | FrontZone | 137 | |
| | RearZone | 588 | |
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| ### ✏️ Annotation |
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| - Fully manual annotation |
| - Consistent labeling across frames |
| - Handled occlusion, overlap, and tandem proximity |
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| ### 🔧 Augmentation |
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| - 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** |
| <img src='results_cars.png' width="800"> |
| <img src='confusion_matrix_cars.png' width="800"> |
| **Zone Results** |
| <img src='results_zone.png' width="800"> |
| <img src='confusion_matrix_zone.png' width="800"> |
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| ## 🧠 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 |
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| ### 🟣 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 |
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| ### 🚫 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. |
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