Traffic Monitoring System

🔍 Overview

A sophisticated traffic analysis backend that leverages computer vision and machine learning to monitor and process traffic camera footage in real-time. This system detects, counts, and tracks vehicles across multiple traffic cameras, providing valuable insights into traffic patterns and density.

✨ Key Features

• Automated Video Acquisition: Downloads videos from multiple traffic cameras defined in a configuration file
• Real-time Vehicle Detection: Utilizes YOLOv5 to accurately identify cars, buses, and trucks in video streams
• Concurrent Processing Pipeline: Implements multi-threading for efficient processing of multiple video streams
• RESTful API: Exposes endpoints to retrieve processed video data and vehicle statistics
• Scalable Architecture: Designed to handle multiple camera groups with independent processing

🛠️ Technology Stack

• Python: Core programming language
• YOLOv5: State-of-the-art object detection model
• OpenCV: Computer vision library for video processing
• Flask: Lightweight web framework for API endpoints
• Threading: Parallel processing of video streams
• FFmpeg: Video conversion and manipulation
• MoviePy: Python library for video editing
• JSON: Data storage and API response format

🏗️ System Architecture

The system follows a modular design with distinct components:

1. Video Downloader Module

   • Fetches videos from configured traffic cameras
   • Handles network requests and error recovery
   • Organizes videos by camera groups

2. Vehicle Detection Engine

   • Processes video frames through YOLOv5
   • Identifies and counts vehicles by category
   • Generates annotated video output with detection boxes

3. API Layer

   • Provides endpoints for requesting processed data
   • Returns vehicle counts and processed videos
   • Supports filtering by camera group ID

⚡ Performance Optimizations

• Multi-threaded video processing for concurrent operations
• Efficient resource management through thread pooling
• Optimized video processing pipeline with intelligent frame handling
• JSON-based caching of results for quick retrieval

💎 Implementation Highlights

• Concurrent Processing: Uses threading to download and process multiple videos simultaneously
• Error Handling: Robust mechanisms to handle network failures and corrupt video files
• Video Post-Processing: Advanced processing including trimming and format conversion
• Flexible Configuration: JSON-based configuration for easy system adaptation

📦 Installation and Setup

# Clone the repository
git clone https://github.com/yourusername/backend-traffic.git
cd backend-traffic

# Install dependencies
pip install -r requirements.txt

# Run the application
python main.py

🔌 API Usage

Get Processed Video Data

GET /get?id=<camera_group_id>

Returns processed video data and vehicle counts for the specified camera group.

🚀 Future Enhancements

• Traffic density heatmaps
• Vehicle speed estimation
• Traffic anomaly detection
• Historical data analysis and pattern recognition
• Integration with traffic management systems

📄 License

MIT License