Detecting gaze interaction in video sequences
The research about exploring the interaction of human action has been popular in the field of computer vision. Specially, detecting whether people are looking at each other offers great help to figure out the relationship between people in video sequences. In this dissertation, our first metho...
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| 其他作者: | |
| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/140561 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | The research about exploring the interaction of human action has been popular in the
field of computer vision. Specially, detecting whether people are looking at each other
offers great help to figure out the relationship between people in video sequences. In
this dissertation, our first method is LAEO-Net with slightly change to fit in our
dataset’s meeting environment, including head detection, track of head position and
final bin-classification. It is made up of three branches, two of them employ 3D
convolutional layers to extract the characteristic and the other applies 2D
convolutional layers for head relative position. Besides, we propose another method
to do the auxiliary determination. It can be divided in three different mathematical
models based on the multi-loss head pose estimation. Three Euler angels presenting
head pose are obtained by the network separately, in order to improve the accuracy of
the final results. In the part of evaluation, we choose UCO-LAEO and AVA-LAEO
datasets to do the training and compare above methods on TVHID dataset and do the
analysis according to respective advantages and weakness. |
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