An Efficient System to Collect Data for AI Training on Multi-Category Object Counting Task

This study focused on the problem of collecting data to train AI for a multi-category object counting task, which is like a standard object counting task, where we need to count the number of a particular type of object, for instance, counting how many people in an image. In multi-category object counting, we need to count more than one type of object by keeping track of the number of each type of object. Although in the real object counting case, we often find that AI needs to count objects with multiple categories, the dataset for that particular task is not publicly available. Meanwhile, to have a robust AI for this task, it needs to be trained with a massive amount of data. Therefore, in this study, we developed a system to efficiently facilitate massive data collection for multi-category object counting. This aim was achieved by a careful design of the user experience in the system. The system has been proved to be useful via Technology Acceptance Model (TAM).