Smart Traffic Management System

 Introduction This project is a Smart Traffic Management System built using Python, Flask, OpenCV, SQLAlchemy, and multithreading. The system processes video feeds from multiple cameras, detects vehicle density, dynamically controls traffic signals, and logs traffic data into a database. Project Flow Video Input – Multiple cameras (or video files) provide live feeds. Processing – Each frame is analyzed using OpenCV to count vehicles. Signal Logic – Based on vehicle density, traffic lights are dynamically switched. Database Logging – Vehicle counts are stored every minute in SQLite. Web Dashboard – Flask renders live video, API endpoints, and a dashboard for monitoring. Code Explanation 1. Importing Libraries import cv2, time, threading, numpy as np from datetime import datetime, timedelta from flask import Flask, render_template, Response, jsonify from flask_sqlalchemy import SQLAlchemy from sqlalchemy import UniqueConstraint Op...

AI-Powered Traffic Management System Ever found yourself stuck at a red light on an empty street, wondering why the system isn't smarter? I'm excited to share my solution: an AI-Powered Smart Traffic Management System . This isn't just a concept; it's a fully functional prototype designed to reduce congestion and improve traffic flow in real-time. Key Features Real-Time Vehicle Detection: Utilizes OpenCV to process live video streams and accurately count vehicles in each lane. Adaptive Signal Control: Dynamically adjusts signal timings based on vehicle counts, giving priority to lanes with heavier traffic. Live Web Dashboard: A Flask-based dashboard visualizes live camera feeds, vehicle counts, and signal status, providing a real-time overview of traffic flow. Data Logging & Analytics: Logs traffic density to a SQL database for historical analysis, performance monitoring, and long-term city planning. How It Works: The Tech Stack Backend: Python with the Flask m...