Video object segmentation and tracking framework with improved threshold decision and diffusion distance
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Journal Volume
23
Journal Issue
6
Pages
921-934
Date Issued
2013
Author(s)
Abstract
Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multibackground model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multibackground model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues. © 1991-2012 IEEE.
Subjects
Diffusion distance (DD); particle filter; smart camera; surveillance; threshold decision; tracking
Other Subjects
Diffusion distance; Likelihood functions; Particle filter; Smart cameras; Smart surveillance systems; threshold decision; Video object tracking; Video-object segmentation; Algorithms; Clutter (information theory); Diffusion; Monte Carlo methods; Security systems; Space surveillance; Surface discharges; Tracking (position); Motion compensation
Type
journal article