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README.md

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+# 🏎 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**.
+
+---
+
+## 🧠 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.
+
+---
+
+## 🎯 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  
+
+---
+
+## 📊 Training Data
+
+### 📁 Source
+
+- Formula Drift Seattle 2025 PRO, Round 6 - Top 32  
+https://www.youtube.com/watch?v=MuD-uxGQnrg&t=879s  
+
+---
+
+### 🔢 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  
+
+---
+
+### 🏷 Class Distribution
+
+| Class      | Count |
+|-----------|------|
+| Leader    | 1,204 |
+| Chaser    | 1,201 |
+| FrontZone | 137   |
+| RearZone  | 588   |
+
+---
+
+### ✏️ Annotation
+
+- Fully manual annotation  
+- Consistent labeling across frames  
+- Handled occlusion, overlap, and tandem proximity  
+
+---
+
+### 🔧 Augmentation
+
+- Rotation ± 8°
+- Saturation ± 15%
+- Brightness ± 10%
+- Blur 2px
+- Mosaic = 0.2
+- Scale ± 15%
+- Translate ± 5% 
+- Hsv_h  (Color tint) =  0.01
+  
+---
+## ⚙️ 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  
+---
+## 📈 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 |
+---
+## 📉 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">
+---
+## 🧠 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.
+---
+## ⚠️ 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  
+---
+### 📉 Weak Areas
+- Zone boundary precision  
+- Leader vs. chaser ambiguity in tight proximity  
+---
+### 📊 Data Bias
+- Single track (Evergreen Speedway)  
+- Single event and lighting condition  
+- Fixed drone perspective  
+---
+### 🌦 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  
+---
+### 📏 Dataset Limitations
+- Underrepresented zone classes  
+- Limited diversity (track, cars, conditions)  
+- Few edge-case scenarios (spins, collisions)  
+---
+## 🏁 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.