Our client has successfully developed an integrated smart parking system that uses advanced technology to monitor parking space availability in real-time. The system is driven by overhead sensors, intelligent analysis of parking spaces, and real-time data reporting. It is a comprehensive solution that identifies vacant parking spaces across car parks, streets, parking fields, offices, airports, hospitals, and more. Using GPS technology, the application informs the user of the nearest available parking space to make parking easier and more efficient.
The client's goal was to revolutionize the way of parking with an application that accurately identifies the nearest available parking spots, checks for parking availability, and determines the type of vehicle parked in each spot to optimize parking efficiency. They had successfully developed the application and were now focused on training the machine learning algorithm to enhance its accuracy and efficiency.
Once the machine learning phase is complete, the system will be able to learn and predict parking patterns and automatically optimize parking availability. To fulfill this, the client was in search of a reliable partner to handle the large volume of training data and perform image labeling in real time for the machine learning phase of their smart parking app solution. The client needed to ensure that the annotated data was of high quality and could support the development of a robust and reliable algorithm.
Upon browsing SunTec’s website and speaking to their consultant, we decided to give over the project to the team. We suggested going with dedicated team engagement for effective collaboration and quick project completion. It was hard to find a team that could work 24*7 and label images in real-time but SunTec's team was able to provide us with a solution that worked seamlessly for our requirements. Since the team had already worked on numerous data annotation projects in the past, we were confident that their skilled data annotators could help us meet our application goals.
However, at the initial stage, a few project challenges were:
Differences in the dimensions of the sensor images and map images
Software's 3D view of motor vehicles not matching the parking area
Incorrect installation of sensor height in some locations leading to discrepancies in images
Overlapping of image frames of different motor vehicles
Having understood the requirements and challenges involved in the project, we decided to form a dedicated team of 38 competent image annotators, working in three shifts to ensure a round-the-clock service for the client (enabling the client’s application to get access to updated parking slots available in real-time).
The live image feed captured from the sensors was constantly uploaded to the client's tool. Our team (who had access to the tool) monitored the image dataset closely, analyzed these images, and filtered out irrelevant, blurred, and low-quality images. This helped us access an efficient and optimized dataset for the image labeling process.
Our team of professionals utilized the bounding box technique to meticulously label empty and occupied parking spots in our client's tool at the speed of 600 FPH. In addition to parking slots, our team also annotated vehicles, parking lines, people, obstacles, trees, signs, and other important objects to provide our algorithm with a holistic understanding of the image. This rigorous approach enabled us to create a model that can accurately recognize all objects present in the image and work effectively.
SunTec's approach highlights the importance of human intervention in AI-driven systems, emphasizing the need for collaboration between humans and machines to achieve optimal results. Although the project involved developing an AI-driven smart parking system, it relied heavily on human input and intervention at several stages. Our human-in-the-loop approach ensured that the system's accuracy was continuously monitored and adjusted by human annotators, making it more reliable and robust. Additionally, the validation process involved a combination of manual and automated methods to ensure the correctness of the labeled data.
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