Our client has created an innovative smart parking system that leverages advanced technology to track parking space availability in real time. The system utilizes overhead sensors and intelligent analysis to monitor parking spaces and provides live data updates. This comprehensive solution identifies vacant parking spots across a variety of locations, including car parks, streets, office buildings, airports, hospitals, and more. By integrating GPS technology, the application guides users to the nearest available parking space, simplifying and optimizing the parking experience.
The client aimed to revolutionize parking with an application that accurately identifies the nearest available parking spots, checks parking availability, and determines the type of vehicle parked in each spot to enhance efficiency. With the application already developed, they now sought to train a machine learning algorithm to further improve its accuracy and performance.
Once the machine learning phase is complete, the system will be able to predict parking patterns and autonomously optimize parking availability. To achieve this, the client required a reliable partner for data annotation services to manage the large volume of training data and perform real-time image labeling during the machine learning phase. Ensuring high-quality annotated data was essential for building a robust and reliable algorithm.
At the outset, the project faced a few challenges:
“After reviewing Adobe website and consulting with their team, we decided to entrust the project to them. We opted for a dedicated team engagement model to ensure effective collaboration and rapid project completion. Finding a team capable of working 24/7 and labeling images in real-time was a challenge, but Adobe team provided a seamless solution tailored to our needs. Given their extensive experience with data annotation projects, we were confident that their skilled annotators would help us achieve our application goals.”
Adobe approach emphasizes the critical role of human intervention in AI-driven systems, underscoring the importance of collaboration between humans and machines for achieving optimal outcomes. While the project focused on developing an AI-powered smart parking system, it heavily relied on human input at key stages. Our human-in-the-loop approach ensured that the system’s accuracy was continuously monitored and refined by skilled annotators, enhancing its reliability and robustness. Moreover, the validation process combined both manual and automated techniques to guarantee the accuracy of the labeled data.
After thoroughly understanding the project requirements and challenges, we assembled a dedicated team of 38 skilled image annotators working in three shifts. This round-the-clock approach ensured that the client’s application consistently received real-time updates on available parking slots.
