earticle

영어

Often most of the modern human people are suffering from a long time of working or studying on the stationary pose. Subsequently, the health of our life is highly threatened to be exacerbated by chronic orthopedic diseases. In order to solve this social problem, we suggest pose detection that can have the people who have deleterious postures be notified. By using nowadays advanced computer vision techniques, in this paper we suggest the posture recognition module to enhance our quality of life. While most posture recognition recognizes only one person's posture, we made our pipeline to perform posture recognition for multiple people through images obtained through a single camera. One of the big problems in measuring people's postures is that it is necessary to distinguish the various body structures and postures of people. For this, posture images and labeling of various people are required. We created pose images of people of various body types through images of a small number of people through skeleton-based coordinates augmentation. We made a posture classifier using various models and observed the improvement of augmentation performance for each model. Through this, we found that the postures of various people can be measured using a relatively small data set. In particular, for deep-learning models that require a lot of data, generalization performance was greatly improved.