Team Name:
After Realism
Team Members:
Have you ever found yourself driving around looking for a parking spot? And by the time you find one, it’s far away from where you need to be, and you’re already running late? This is a problem many of us face every day, and it costs individuals, businesses and the economy millions of dollars. According to academic literature, the average commuter spends at least 30 minutes per day trying to find a parking spot. Many have tried and failed to solve this problem before, however they are often restricted by high infrastructure costs. That is where our solution differs.
- Using AI/ML and IoT (surveillance/traffic cameras) we will build an application which shows customers which parking spaces are free, and allows them to book and pay through the application for a specific parking spot. This will save the average ANU student 45 minutes per day in time, stress (very stressful experience trying to find a park) and money (reduced number of parking fines).
- Our trained neural net has a prediction accuracy of 95% on average across all weather conditions (rain, wind, overcast etc.) Based on a dataset of 695,000 images.
Description of Use Help us identify the problem and short down solution
Eligibility: Use any open dataset to support your entry.
Go to Challenge | 45 teams have entered this challenge.
Eligibility: Use any open dataset to support your entry.
Go to Challenge | 39 teams have entered this challenge.
Eligibility: Must use at least ONE relevant/related dataset from www.data.act.gov.au
Go to Challenge | 21 teams have entered this challenge.
