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README.md

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+# **Traffic Intersection Monitoring System with OpenVINO**
+
+This project implements a real-time traffic monitoring solution that detects vehicles, pedestrians, and traffic violations at intersections using object detection models optimized with OpenVINO. It features a PyQt5-based dashboard for visualization and control, integrates synthetic data generation using CARLA, and supports enhanced scene understanding through vision-language models.
+
+## Problem Statement
+
+The system monitors traffic intersections to identify and track vehicles, pedestrians, and cyclists in real-time. It collects traffic statistics and detects violations such as red-light running and jaywalking. The focus is on efficient deployment at the edge using Intel hardware.
+
+## Objectives
+
+- Detect vehicles, pedestrians, and cyclists using object detection
+- Monitor and record traffic violations in real-time
+- Display detection results and statistics through a graphical interface
+- Enable model deployment using OpenVINO for optimized inference
+- Generate and annotate synthetic traffic data using CARLA
+- Integrate visual reasoning capabilities through vision-language models
+
+## Training and Optimization
+
+1. **Model Training**  
+   The YOLOv12 model is trained using PyTorch with labeled image data representing traffic scenes.
+
+2. **Export Pipeline**  
+   The trained model is exported to ONNX format, and then converted to OpenVINO's Intermediate Representation (IR) format.
+
+3. **Optimization**  
+   Post-training quantization is applied to convert the model from FP32 to INT8, improving inference speed while maintaining accuracy.
+
+4. **Deployment**  
+   OpenVINO's InferRequest API is used for asynchronous inference, enabling efficient frame-by-frame processing suitable for real-time applications.
+
+## Synthetic Data Generation
+
+CARLA is used to simulate traffic intersections with accurate layouts, signage, and weather variations. It supports:
+
+- Scene diversity through environmental changes (rain, fog, glare, nighttime)
+- Simulation of pedestrian and vehicle behaviors (red-light running, jaywalking)
+- Automatic annotation of bounding boxes and class labels for use with object detection models
+
+## Vision-Language Integration
+
+Two models are integrated to enhance scene understanding:
+
+- **BLIP-2**: Automatically generates text summaries of visual scenes (e.g., “A vehicle is crossing the red light”)
+- **LLaVA**: Enables question-answering over video frames (e.g., “Why was the pedestrian flagged?”)
+
+These tools allow human operators to interact with the system more effectively by supporting natural language explanations and queries.
+
+## PyQt5-Based Dashboard
+
+The dashboard enables real-time interaction with the monitoring system and includes:
+
+- Live video feed with overlayed bounding boxes
+- Detection tags for pedestrians, vehicles, and violators
+- Violation statistics and traffic flow metrics
+- Controls for switching between camera sources and simulated environments
+- High-performance rendering using QPainter for dynamic visual updates