diff --git a/REFACTOR_PLAN.md b/REFACTOR_PLAN.md
index 42bffda..47c40f3 100644
--- a/REFACTOR_PLAN.md
+++ b/REFACTOR_PLAN.md
@@ -238,32 +238,49 @@ core/
 - ✅ **Production Ready**: Stable concurrent streaming from multiple camera sources
 - ✅ **Dependencies**: Added opencv-python, numpy, and requests to requirements.txt
 
+### 3.4 Recent Streaming Enhancements (Post-Phase 3)
+- ✅ **Format-Specific Optimization**: Tailored for 1280x720@6fps RTSP streams and 2560x1440 HTTP snapshots
+- ✅ **H.264 Error Recovery**: Enhanced error handling for corrupted frames with automatic stream recovery
+- ✅ **Frame Validation**: Implemented corruption detection using edge density analysis
+- ✅ **Buffer Size Optimization**: Adjusted buffer limits to 3MB for RTSP frames (1280x720x3 bytes)
+- ✅ **FFMPEG Integration**: Added environment variables to suppress verbose H.264 decoder errors
+- ✅ **URL Preservation**: Maintained clean RTSP URLs without parameter injection
+- ✅ **Type Detection**: Automatic stream type detection based on frame dimensions
+- ✅ **Quality Settings**: Format-specific JPEG quality (90% for RTSP, 95% for HTTP)
+
 ## ✅ Phase 4: Vehicle Tracking System - COMPLETED
 
 ### 4.1 Tracking Module (`core/tracking/`)
-- ✅ **Create `tracker.py`** - Vehicle tracking implementation
+- ✅ **Create `tracker.py`** - Vehicle tracking implementation (305 lines)
   - ✅ Implement continuous tracking with configurable model (front_rear_detection_v1.pt)
   - ✅ Add vehicle identification and persistence with TrackedVehicle dataclass
   - ✅ Implement tracking state management with thread-safe operations
   - ✅ Add bounding box tracking and motion analysis with position history
+  - ✅ Multi-class tracking support for complex detection scenarios
 
-- ✅ **Create `validator.py`** - Stable car validation
+- ✅ **Create `validator.py`** - Stable car validation (417 lines)
   - ✅ Implement stable car detection algorithm with multiple validation criteria
   - ✅ Add passing-by vs. fueling car differentiation using velocity and position analysis
   - ✅ Implement validation thresholds and timing with configurable parameters
   - ✅ Add confidence scoring for validation decisions with state history
+  - ✅ Advanced motion analysis with velocity smoothing and position variance
 
-- ✅ **Create `integration.py`** - Tracking-pipeline integration
+- ✅ **Create `integration.py`** - Tracking-pipeline integration (547 lines)
   - ✅ Connect tracking system with main pipeline through TrackingPipelineIntegration
   - ✅ Handle tracking state transitions and session management
   - ✅ Implement post-session tracking validation with cooldown periods
   - ✅ Add same-car validation after sessionId cleared with 30-second cooldown
+  - ✅ Car abandonment detection with automatic timeout monitoring
+  - ✅ Mock detection system for backend communication
+  - ✅ Async pipeline execution with proper error handling
 
 ### 4.2 Testing Phase 4
 - ✅ Test continuous vehicle tracking functionality
 - ✅ Test stable car validation logic
 - ✅ Test integration with existing pipeline
 - ✅ Verify tracking performance and accuracy
+- ✅ Test car abandonment detection with null detection messages
+- ✅ Verify session management and progression stage handling
 
 ### 4.3 Phase 4 Results
 - ✅ **VehicleTracker**: Complete tracking implementation with YOLO tracking integration, position history, and stability calculations
@@ -274,6 +291,10 @@ core/
 - ✅ **Configurable Parameters**: All tracking parameters are configurable through pipeline.json
 - ✅ **Session Management**: Complete session lifecycle management with post-fueling validation
 - ✅ **Statistics and Monitoring**: Comprehensive statistics collection for tracking performance
+- ✅ **Car Abandonment Detection**: Automatic detection when cars leave without fueling, sends `detection: null` to backend
+- ✅ **Message Protocol**: Fixed JSON serialization to include `detection: null` for abandonment notifications
+- ✅ **Streaming Optimization**: Enhanced RTSP/HTTP readers for 1280x720@6fps RTSP and 2560x1440 HTTP snapshots
+- ✅ **Error Recovery**: Improved H.264 error handling and corrupted frame detection
 
 ## 📋 Phase 5: Detection Pipeline System