chore: update refactor plan
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			@ -238,32 +238,49 @@ core/
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- ✅ **Production Ready**: Stable concurrent streaming from multiple camera sources
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- ✅ **Dependencies**: Added opencv-python, numpy, and requests to requirements.txt
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### 3.4 Recent Streaming Enhancements (Post-Phase 3)
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- ✅ **Format-Specific Optimization**: Tailored for 1280x720@6fps RTSP streams and 2560x1440 HTTP snapshots
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- ✅ **H.264 Error Recovery**: Enhanced error handling for corrupted frames with automatic stream recovery
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- ✅ **Frame Validation**: Implemented corruption detection using edge density analysis
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- ✅ **Buffer Size Optimization**: Adjusted buffer limits to 3MB for RTSP frames (1280x720x3 bytes)
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- ✅ **FFMPEG Integration**: Added environment variables to suppress verbose H.264 decoder errors
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- ✅ **URL Preservation**: Maintained clean RTSP URLs without parameter injection
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- ✅ **Type Detection**: Automatic stream type detection based on frame dimensions
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- ✅ **Quality Settings**: Format-specific JPEG quality (90% for RTSP, 95% for HTTP)
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## ✅ Phase 4: Vehicle Tracking System - COMPLETED
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### 4.1 Tracking Module (`core/tracking/`)
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- ✅ **Create `tracker.py`** - Vehicle tracking implementation
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- ✅ **Create `tracker.py`** - Vehicle tracking implementation (305 lines)
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  - ✅ Implement continuous tracking with configurable model (front_rear_detection_v1.pt)
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  - ✅ Add vehicle identification and persistence with TrackedVehicle dataclass
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  - ✅ Implement tracking state management with thread-safe operations
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  - ✅ Add bounding box tracking and motion analysis with position history
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  - ✅ Multi-class tracking support for complex detection scenarios
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- ✅ **Create `validator.py`** - Stable car validation
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- ✅ **Create `validator.py`** - Stable car validation (417 lines)
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  - ✅ Implement stable car detection algorithm with multiple validation criteria
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  - ✅ Add passing-by vs. fueling car differentiation using velocity and position analysis
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  - ✅ Implement validation thresholds and timing with configurable parameters
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  - ✅ Add confidence scoring for validation decisions with state history
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  - ✅ Advanced motion analysis with velocity smoothing and position variance
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- ✅ **Create `integration.py`** - Tracking-pipeline integration
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- ✅ **Create `integration.py`** - Tracking-pipeline integration (547 lines)
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  - ✅ Connect tracking system with main pipeline through TrackingPipelineIntegration
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  - ✅ Handle tracking state transitions and session management
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  - ✅ Implement post-session tracking validation with cooldown periods
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  - ✅ Add same-car validation after sessionId cleared with 30-second cooldown
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  - ✅ Car abandonment detection with automatic timeout monitoring
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  - ✅ Mock detection system for backend communication
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  - ✅ Async pipeline execution with proper error handling
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### 4.2 Testing Phase 4
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- ✅ Test continuous vehicle tracking functionality
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- ✅ Test stable car validation logic
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- ✅ Test integration with existing pipeline
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- ✅ Verify tracking performance and accuracy
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- ✅ Test car abandonment detection with null detection messages
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- ✅ Verify session management and progression stage handling
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### 4.3 Phase 4 Results
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- ✅ **VehicleTracker**: Complete tracking implementation with YOLO tracking integration, position history, and stability calculations
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			@ -274,6 +291,10 @@ core/
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- ✅ **Configurable Parameters**: All tracking parameters are configurable through pipeline.json
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- ✅ **Session Management**: Complete session lifecycle management with post-fueling validation
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- ✅ **Statistics and Monitoring**: Comprehensive statistics collection for tracking performance
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- ✅ **Car Abandonment Detection**: Automatic detection when cars leave without fueling, sends `detection: null` to backend
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- ✅ **Message Protocol**: Fixed JSON serialization to include `detection: null` for abandonment notifications
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- ✅ **Streaming Optimization**: Enhanced RTSP/HTTP readers for 1280x720@6fps RTSP and 2560x1440 HTTP snapshots
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- ✅ **Error Recovery**: Improved H.264 error handling and corrupted frame detection
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## 📋 Phase 5: Detection Pipeline System
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