Segmenting and tracking objects in video sequences based on graphical probabilistic models

Abstract

Segmenting and tracking objects in video sequences is important in vision-based application areas, but the task could be difficult due to the potential variability such as object occlusions and illumination variations. In this thesis, three techniques of segmenting and tracking objects in image sequences are developed based on graphical probabilistic models (or graphical models), especially Bayesian networks and Markov random fields. First, this thesis presents a unified framework for video segmentation based on graphical models. Second, this work develops a dynamic hidden Markov random field (DHMRF) model for foreground object and moving shadow segmentation. Third, this thesis proposes a switching hypothesized measurements (SHM) model for multi-object tracking. By means of graphical models, the techniques deal with object segmentation and tracking from relatively comprehensive and general viewpoints, and thus can be universally employed in various application areas. Experimental results show that the proposed approaches robustly deal with the potential variability and accurately segment and track objects in video sequences.