The Development of Long-Distance Viewing Direction Analysis and Recognition of Observed Objects Using Head Image and Deep Learning

Yu-Shiuan Tsai, Nai-Chi Chen, Yi-Zeng Hsieh and Shih-Syun Lin
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Yu-Shiuan Tsai: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
Nai-Chi Chen: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
Yi-Zeng Hsieh: Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
Shih-Syun Lin: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan

Mathematics, 2021, vol. 9, issue 16, 1-12

Abstract: 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.

Keywords: deep learning; long-distance perspective analysis; single camera; observed objects; OpenPose; head image (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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