Description

Smart Crosswalk System

Smart Crosswalk System
Introduction
This project aims to design and implement a Smart Crosswalk System that leverages AI, real-time monitoring, and renewable energy to improve pedestrian safety at non-signalized intersections. The system will use cameras combined with AI models (YOLOv8 and pose estimation) to detect pedestrians, especially children, approaching a crosswalk and intending to cross. LED-embedded traffic signs powered by solar panels will serve as visual alerts for drivers, ensuring they have sufficient time to brake.
Background
Pedestrian safety at non-signalized intersections is a major concern in urban areas. Drivers often fail to notice pedestrians approaching crosswalks, especially at night or in poor weather conditions. Traditional warning signs lack intelligence and real-time responsiveness. By integrating AI-based pedestrian detection, renewable energy, and IoT technologies, a smarter and more reliable solution can be deployed to reduce accidents and save lives.
Project Scope
• Hardware Integration
- Install 2 AI-powered cameras in both sides of the crosswalk to monitor pedestrian activity. - Deploy LED-based smart traffic signs around the crosswalk for visual driver alerts. - Power the system with solar panels for energy save.
• Software Development
- Build a Python-based AI service using YOLOv8 for human detection and pose estimation to determine if pedestrians are facing the crosswalk and within 2 meters. - Develop a Node.js backend server to handle API routes, communication with the AI service, and control of LED signals with LoRaWAN system. - Implement a React.js frontend dashboard for monitoring detections, logs, and system status. - Store detection events and system logs in MongoDB.
• System Integration
- Establish communication between the Node.js backend and the Python AI service using REST API. - Send detection results from the AI service to the backend in real time. - Trigger LED alerts through backend-controlled LoRaWAN system when pedestrians are detected. - Provide an admin dashboard to visualize detections and manage the system.
Student Requirements
• Familiarity with JavaScript and Python. • Knowledge of React.js and Node.js. • Basic understanding of MongoDB. • Understand the concept of AI/ML frameworks (YOLOv8 and pose estimation). • Understanding of REST API. • Teamwork and independent learning.
Development Tools
• Frontend: React.js, Tailwind / Material UI / CSS. • Backend: Node.js (Express.js). • AI Service: Python (YOLOv8, OpenCV, PyTorch). • Database: MongoDB.
• General technologies: Axios. • Hardware Communication: LoRaWAN system for LED control. • Additional Tools: Postman, GitHub, VSCode.
Deliverables
• AI-based Smart Crosswalk Fullstack Web App: Dashboard with real-time pedestrian detection and system alerts. • Specification document. • YouTube video. • Poster and presentation. • Presentation.
Name: Netanel Ben Hamo
E-mail: netanelbe@hit.ac.il
AI Based Smart Crosswalk System - Full Stack project

AI and Machine Learning

Description

Emphasis in project execution

The project is has cooperation with the industry and combines meeting deadlines while being creative and focused on the task
