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Raspberry Pi Traffic Light Using TensorFlow & Python

A smart traffic light system powered by a Raspberry Pi and TensorFlow that uses computer vision to detect pedestrians waiting at a zebra crossing and dynamically prioritizes them over vehicles.

Originally published on ElectronicsForU

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 5,400.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 810.00
10pcs 5mm RED Diffused Light Emitting Diodes - LEDs Set Rs. 39.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

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How It Works

This intelligent traffic light system operates by combining edge artificial intelligence with real-time computer vision running on a Raspberry Pi 5. The setup centers around the Raspberry Pi connected to a camera module, which continuously captures live video feeds of the pedestrian crossing area. Software libraries including Python 3, OpenCV, and NumPy process these incoming video frames to handle image acquisition and array manipulation, while dlib and TensorFlow execute machine learning inference to recognize human presence and pedestrian intent. When a pedestrian is successfully detected waiting at the zebra crossing, the TensorFlow model triggers a decision-making routine in the Python script. This script then changes the state of the traffic light LEDs via the connected circuit components, transitioning the signal to prioritize foot traffic over oncoming vehicles. Power is supplied steadily to the entire assembly via a dedicated power supply, ensuring continuous operation of both the high-processing-load Raspberry Pi board and the physical signaling hardware.

Why Build This

Design and deploy a prototype smart city pedestrian crossing system that integrates machine learning with physical hardware actuation.

Gain practical experience implementing edge AI algorithms using TensorFlow, OpenCV, and dlib on an undergraduate engineering level.

Explore intelligent urban mobility systems and automated traffic management concepts using modern single-board computers.

Real-World Application

Smart city pedestrian crossings, automated traffic management, and intelligent urban mobility systems.

Skills You'll Learn

Computer vision implementation

Machine learning inference on edge devices

Python programming for hardware control

Real-time video processing

Sensor data integration

Safety Precautions

No specific precautions noted for this build — always follow general electronics safety practices.

Technology Tags

TENSORFLOW

PYTHON

COMPUTER VISION

OPENCV

NUMPY

DLIB

RASPBERRY PI

MACHINE LEARNING

TRAFFIC LIGHT

Frequently Asked Questions

Can I use an older Raspberry Pi model like the Raspberry Pi 4 for this project?

While it may be possible, the project specifically targets the Raspberry Pi 5 due to the heavy computational demands of running TensorFlow and computer vision models simultaneously.

Do I need a specific camera module for the pedestrian detection to work?

Any compatible camera module that interfaces directly with the Raspberry Pi camera port and supports high enough resolution for OpenCV processing will suffice.

How are the traffic light LEDs connected to the Raspberry Pi?

The LEDs and associated circuit components are wired to the general-purpose input/output (GPIO) pins of the Raspberry Pi, allowing the Python script to control their states dynamically based on the AI model's output.

Gallery

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

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