Foreground Segmentation and Human Pose Estimation
Date Issued
2007
Date
2007
Author(s)
Huang, Shih-Shinh
DOI
en-US
Abstract
Human dynamics analysis is currently one of the most active researches in computer vision because it is an important and fundamental component in many applications in the areas of human-computer interaction, such as virtual reality, smart surveillance, and intelligent user interface, etc. In the thesis, two issues which we take into considerations for human dynamics analysis are foreground segmentation and pose estimation.
We present a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to perform foreground segmentation in a more semantic region level, we propose an edge-based change detection algorithm to automatically identify the regions with potential appearance variation due to the motion of objects. Then, we incorporate the motion information
to perform foreground segmentation under a Bayesian framework. Given two consecutive images, the joint probability density function of the motion vector field and foreground segmentation mask is defined based on the constraints including observation likelihood and spatiotemporal constraint and thus is maximized to simultaneously achieve the foreground segmentation and the motion estimation in a mutually beneficial manner.
Human pose is a natural way for a computer system to understand the intention of humans. Here, we want to propose a new statistical framework for estimating human pose by use of a reversible jump Markov Chain Monte Carlo (RJMCMC) approach, which tries to recovering the human body configuration based on its silhouette.
Such problem is formulated as that of computing the maximum a posterior (MAP) of the probability density function of pose configuration given currently observed image. Equivalently, pose inference can be considered as
traversing over the difference subspaces. Using of the data-driven mechanism, the mentioned reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space much more efficiently.
Subjects
人體姿態分析
前景切割
貝式網路
姿態估測
資料驅動
RJMCMC近似演算法
Foreground Segmentation
Pose Estimation
Bayesian Network
Reversible Jump Markov Chain Monte Carlo
Data-Driven Strategy
Type
thesis
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