Dental Treatment Process Action Recognition Dataset

In modern dental medicine, optimizing action recognition during the treatment process is crucial for improving treatment efficiency and accuracy. Currently, traditional methods mainly rely on manual observation, which can be easily influenced by subjective factors, leading to misjudgments and inefficiency. Additionally, there is a lack of automated recognition systems or standard datasets specifically for dental actions in the market. This dataset aims to provide a large number of annotated videos of actions during actual dental treatments to support the development of automated action recognition systems. The data is collected using high-definition cameras in real dental treatment environments to ensure realistic scene simulation, and annotated and reviewed in multiple rounds by a team of professional dentists and video analysis experts to ensure consistency and precision. After annotation, the data is processed and stored in MP4 format, organized by dental treatment steps. The recorded actions, such as brushing, cleaning, and treatment, are sorted and categorized to facilitate training and testing.