http://scholars.ntou.edu.tw/handle/123456789/17753
標題: | The Development of Long-Distance Viewing Direction Analysis and Recognition of Observed Objects Using Head Image and Deep Learning | 作者: | Tsai, Yu-Shiuan Chen, Nai-Chi Hsieh, Yi-Zeng Lin, Shih-Syun |
關鍵字: | deep learning;long-distance perspective analysis;single camera;observed objects;OpenPose;head image | 公開日期: | 1-八月-2021 | 出版社: | MDPI | 卷: | 9 | 期: | 16 | 來源出版物: | MATHEMATICS | 摘要: | In this study, we use OpenPose to capture many facial feature nodes, create a data set and label it, and finally bring in the neural network model we created. The purpose is to predict the direction of the person's line of sight from the face and facial feature nodes and finally add object detection technology to calculate the object that the person is observing. After implementing this method, we found that this method can correctly estimate the human body's form. Furthermore, if multiple lenses can get more information, the effect will be better than a single lens, evaluating the observed objects more accurately. Furthermore, we found that the head in the image can judge the direction of view. In addition, we found that in the case of the test face tilt, approximately at a tilt angle of 60 degrees, the face nodes can still be captured. Similarly, when the inclination angle is greater than 60 degrees, the facing node cannot be used. |
URI: | http://scholars.ntou.edu.tw/handle/123456789/17753 | DOI: | 10.3390/math9161880 |
顯示於: | 資訊工程學系 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。