https://github.com/mikebionic/trafficapp

App for traffic control, security and smart city integration research. Detect and count vehicles from streams on traffic + Calculate and create efficient traffic light control + Secure pedestrians and cars from crashю
https://github.com/mikebionic/trafficapp

arduino image-processing internet-of-things opencv python smart-city traffic-lights

Last synced: 9 months ago
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App for traffic control, security and smart city integration research. Detect and count vehicles from streams on traffic + Calculate and create efficient traffic light control + Secure pedestrians and cars from crashю

• Host: GitHub
• URL: https://github.com/mikebionic/trafficapp
• Owner: mikebionic
• License: mit
• Created: 2020-11-15T18:05:44.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2025-08-12T06:28:51.000Z (10 months ago)
• Last Synced: 2025-08-12T08:27:35.619Z (10 months ago)
• Topics: arduino, image-processing, internet-of-things, opencv, python, smart-city, traffic-lights
• Language: Python
• Homepage: https://medium.com/@mecreate/how-to-build-an-easiest-smart-traffic-app-using-opencv-with-raspberrypi-and-arduino-iot-4af5cb5e1c07
• Size: 10.6 MB
• Stars: 5
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# 🚦 Smart Traffic Control System

**Traffic monitoring, vehicle & pedestrian detection, and intelligent signal management**

## 📌 Project Goals

* **Detect & Count Vehicles** from live traffic camera streams using computer vision.

* **Optimize Traffic Light Timings** based on real-time data for efficient flow.

* **Enhance Safety** for pedestrians and vehicles, minimizing crash risks.

---

## ⚙️ Installation & Setup

### Requirements

* **Arduino IDE** installed and running.

* **Serial Monitor** in Arduino IDE should be open.

* **OpenCV** installed with all necessary dependencies.

* Configured and connected cameras for live traffic monitoring.

### Steps

1. Install Arduino IDE and ensure your microcontroller is connected.

2. Open the Serial Monitor in Arduino IDE.

3. Install OpenCV (`pip install opencv-python`) and configure your cameras.

4. Run the traffic detection script.

---

## Prototype & Implementation

**Example Frames:**

| Clear View                      | Vehicle Detection (1)                 | Vehicle Detection (2)                 |

| ------------------------------- | ------------------------------------- | ------------------------------------- |

| ![Clear view](images/clear.jpg) | ![Deviation 1](images/deviation1.jpg) | ![Deviation 2](images/deviation2.jpg) |

---

## 📄 Documentation

### Markdown Versions

* [Project Info (English)](documentation/project-info_enUS.md)

* [Project Info (Russian)](documentation/project-info_ruRU.md)

* [Project Info (Turkmen)](documentation/project-info_tkTM.md)

### DOCX Versions

* [Smart Traffic (English)](documentation/SmartTraffic_enUS.docx)

* [Smart Traffic (Russian)](documentation/SmartTraffic_ruRU.docx)

* [Smart Traffic (Turkmen)](documentation/SmartTraffic_tkTM.docx)

---

## Features at a Glance

* Real-time **vehicle counting** & **classification**

* Dynamic **traffic light control** for smoother flow

* **Pedestrian detection** to enhance crossing safety

* Flexible **camera input configuration**

* Works with **Arduino-based hardware integration**