Team Vantage's Parking Lot Management System

Goal Statement:

Although there are several parking lot management systems in use today, we felt that the process of finding an available space could be made much easier.

Our goal was to create a system that could save people’s time and sanity by addressing the shortcomings found in currently implemented parking lot management systems.

Introduction:

There are several flaws in most parking lot systems implemented today. For example, most parking lot management systems currently implemented cannot display which specific parking spaces are available. In addition, many of these systems use sensors that are built into the ground. Although these sensors are very accurate, they have to be implemented in every parking space, which can be costly to install and maintain.

Our system can do the work of multiple sensors, detecting the availability of many parking spaces through the use of a single camera. Our system can also provide parking space information to many users via an IoT approach.

Technical approach:

Using a webcam and Raspberry Pi 3, we designed a camera that can perform object recognition. The camera, mounted from above, only needs a power source and Wi-Fi signal to operate. Once in position, the camera detects whether or not parking spaces are occupied, and sends a corresponding signal to a SQL server. From there, a user can use the phone application that we designed to view which parking spaces are available and reserve an unoccupied space for a set amount of time.

About Team Vantage:

Jake Vargas (CpE) - jaketv@uci.edu

     Tasks include: Developed initial object recognition software and established software connection/interaction independent SQL server. Scheduled meeting times and managed deadlines as team leader.

Joshua Guerrero (CpE) - jaguerr4@uci.edu

     Tasks include: Developed phone application, managed SQL server database creation and SQL server hosting.

Zhenjiang (Will) Yu (CpE) - zhenjiay@uci.edu

     Tasks include: Integrated object recognition software to be run on the Raspberry Pi. Further optimized success of object recognition. Managed FPGA wireless connectivity properties.

Pooria Yaghini - UCI mentor/advisor - pooriam@uci.edu

Project Progress:

As of March 12th, 2018, the most current information demonstrating our project details, including project usage and screenshots, can be found at:

https://www.youtube.com/watch?v=G_a-fhJSzpc