Case Study

Real-Time Parking Detection System

An IoT vision system utilizing edge computing for real-time monitoring of parking availability.

The Problem

Legacy parking systems are often analog or laggy. The project aimed to provide instant, visually-verified availability updates to motorists.

The Approach

Deployed the YOLO v3 (You Only Look Once) object detection algorithm on a Raspberry Pi accelerated by an Intel Neural Compute Stick. The system processes Pi camera feeds in real-time, syncing spot statuses to Firebase. A Flutter-based web app consumed this data to provide a live GUI with Google Maps integration.

Technical Stack

Raspberry PiYOLO Object DetectionIntel Neural Compute StickFirebaseFlutterComputer VisionIoT

Challenges & Constraints

Optimizing deep learning models for edge hardware and ensuring low-latency updates between the Pi and the Firebase real-time database.

Outcome & Learnings

Developed a fully functional prototype that tracked parking occupancy with 95% accuracy and broadcasted updates in under 2 seconds.