921 research outputs found
Feature regularization and learning for human activity recognition.
Doctoral Degree. University of KwaZulu-Natal, Durban.Feature extraction is an essential component in the design of human activity
recognition model. However, relying on extracted features alone for learning often makes the model a suboptimal model. Therefore, this research
work seeks to address such potential problem by investigating feature regularization. Feature regularization is used for encapsulating discriminative
patterns that are needed for better and efficient model learning. Firstly, a
within-class subspace regularization approach is proposed for eigenfeatures
extraction and regularization in human activity recognition. In this ap-
proach, the within-class subspace is modelled using more eigenvalues from
the reliable subspace to obtain a four-parameter modelling scheme. This
model enables a better and true estimation of the eigenvalues that are distorted by the small sample size effect. This regularization is done in one
piece, thereby avoiding undue complexity of modelling eigenspectrum differently. The whole eigenspace is used for performance evaluation because
feature extraction and dimensionality reduction are done at a later stage
of the evaluation process. Results show that the proposed approach has
better discriminative capacity than several other subspace approaches for
human activity recognition. Secondly, with the use of likelihood prior probability, a new regularization scheme that improves the loss function of deep
convolutional neural network is proposed. The results obtained from this
work demonstrate that a well regularized feature yields better class discrimination in human activity recognition. The major contribution of the
thesis is the development of feature extraction strategies for determining
discriminative patterns needed for efficient model learning
FML: Face Model Learning from Videos
Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ,
Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19
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