| dc.description.abstract | Background modeling algorithms are very popular in vision-based systems of both
research and casual activities. Their range of applications are wide and have included not only
video inpainting, video privacy protection, and computational photography, but also foreground
extractions and motion analytic tasks such as those in human behavior monitoring systems, or
observations of animal or insect behaviors. This prevalence can be clearly observed among
statistical background modeling methods such as those employing the Gaussian Mixture
Models (GMM). Not only can their advantages be attributed to their efficient processing on
clear mathematical foundations for fair context modeling, but their formulation also entails
pixel-wise generalistic adaptation to scene dynamics, which has been demonstrably successful
across many cases. However, by the lack of knowledge about scene semantics, their
indiscriminately generalistic adaptiveness is plauged by a dilemma between incorporation of
true background variations and avoidance of long-lived foreground corruptions. Furthermore,
while many modern technological advances have been proposed regarding parallel paradigms
of computing on data-driven learning, statistical GMM methods generally follow only
sequential computing models even on high image resolution. In this thesis, we propose a tensordriven framework of statistical GMM background modeling called TensorMoG, along with its
extensions in considerations of the aforementioned issues. Specifically, from TensorMoG’s
explicit parallelism for background modeling on videos, we introduce High-Variational
Removal (HVR) and Adaptive Learning (ADA) plug-ins to maintain model robustness against
signal noises and allow for adaptiveness in different manners. Then, in order to explicitly
institute scene semantics with spatio-temporal consistency to the domain for context selective
update, we adopt into TensorMoG a generalizable, data-driven maintenance component called
Neural Poisson Inpainter (NPI). NPI is used to inpaint error regions by solving Poisson
problems on images, and thus maintain a semi-supervised, spatio-temporally stable background
modeling process on GMM. Our results with indicate decent quality in background modeling
with our proposed approaches with real-time processing on GPU. | en_US |
