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  • Deep Learning

AI-Based Intelligent Traffic Management System

A computer vision system that automates traffic monitoring, signal control, and violation detection from CCTV.

Overview

YOLO detects and classifies vehicles from live CCTV streams, lane-wise density estimation dynamically adjusts signal timing, ambulances are detected to create a green corridor, and violations — missing helmets, triple riding, signal jumping — are flagged and logged with evidence capture.

Problem Statement

Traffic signals run on fixed timers that ignore actual lane density, so empty lanes hold green while queues build elsewhere. Emergency vehicles wait in the same jams, and violations go unrecorded unless an officer is physically present.

Proposed Solution

Use the CCTV infrastructure already installed to drive signal timing from real density, detect and prioritise emergency vehicles automatically, and capture violation evidence without human presence.

Key Features

  • YOLO-Based Vehicle Detection

    Real-time detection and classification from CCTV streams.

  • Adaptive Signal Control

    Lane-wise density estimation dynamically adjusts signal timing.

  • Emergency Vehicle Priority

    Detects ambulances and creates a green corridor.

  • Violation Detection & Logging

    Flags helmet absence, triple riding, and signal jumping with evidence capture.

Technologies Used

Computer Vision

  • YOLOv8
  • OpenCV
  • Ultralytics
  • Object tracking

Control logic

  • Density estimation
  • Adaptive timing algorithm
  • Python

Application

  • Flask
  • RTSP streaming
  • SQLite
  • Dashboard

Project Workflow

  1. 1

    Stream ingestion

    Live CCTV feeds are decoded frame by frame.

  2. 2

    Vehicle detection

    YOLO detects and classifies vehicles per lane.

  3. 3

    Density estimation

    Lane occupancy drives adaptive signal timing.

  4. 4

    Priority handling

    Detected ambulances trigger a green corridor.

  5. 5

    Violation logging

    Offences are captured with photographic evidence.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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