VECTOR REPRESENTATION TO ENHANCE POSE ESTIMATION FROM RGB IMAGES
thesisposted on 03.05.2020 by Zongcheng Chu
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Head pose estimation is an essential task to be solved in computer vision. Existing research for pose estimation based on RGB images mainly uses either Euler angles or quaternions to predict pose. Nevertheless, both Euler angle- and quaternion-based approaches encounter the problem of discontinuity when describing three-dimensional rotations. This issue makes learning visual pattern more difﬁcult for the convolutional neural network(CNN) which, in turn, compromises the estimation performance. To solve this problem, we introduce TriNet, a novel method based on three vectors converted from three Euler angles(roll, pitch, yaw). The orthogonality of the three vectors enables us to implement a complementary multi-loss function, which effectively reduces the prediction error. Our method achieves state-of-the-art performance on the AFLW2000, AFW and BIWI datasets. We also extend our work to general object pose estimation and show results in the experiment part.