Restaurant Learning Posture Image Dataset

In the current educational training industry, a good sitting posture is crucial for the physical and mental health of children. However, due to the lack of monitoring methods, it is difficult for teachers to promptly correct poor student postures, which over time can lead to health issues such as scoliosis. Existing solutions mostly rely on manual monitoring and do not effectively utilize technology for real-time guidance. The Restaurant Learning Posture Image Dataset aims to provide high-quality visual data to aid in the development of automated posture recognition and correction systems, thereby meeting actual educational and health needs. The dataset is collected through high-definition camera equipment in a standard classroom environment, including posture images at different times. Quality control measures include multiple rounds of annotation and expert review processes to ensure data accuracy and consistency. The annotation team consists of professionals with backgrounds in education and health sciences, with a team size of over 20 people. Data preprocessing includes multiple steps such as image denoising and posture normalization, and the data is organized efficiently in cloud storage using JPEG format.