A Human Identification System Based on Skeleton Key Points Data in The Scenario of People Running on a Treadmill

• Main Authors: Tsai, Cheng-En, 蔡承恩
• Other Authors: Ching, Yu-Tai
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/89dr3t

碩士 === 國立交通大學 === 數據科學與工程研究所 === 107 === This article is based on analyzing the data of skeleton key points, that are transformed by 2D videos of people running on treadmills. The goal is to use these data from skeleton key points to identify a person and a position they are in. The reason why we discard the video and use the skeleton data is because there are some privacy issues. If we put a camera at home, it might record people naked walking in the room, but they don’t want this to be saved. To avoid this, my research is going to use only skeleton data to identify a person. This research is separated in three parts. In the first part, I recorded videos of people running on treadmills using four different camera positions. I tried to find the key points which can tell the differences between people and used these key points to build models that can identify a person. In the second part, I tried to transform one position’s data to another, so that I can identify a person in different camera position by using only one model. In the last part, I tried to train a model to show which position is recorded. After these researches, I have a method to show different person running on the treadmill in different positions.