文章基本信息

标题：Visual Tracking Algorithm Based on Probabilistic Graphical Model
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作者：Mingjie Zhang ; Baosheng Kang
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：9
页码：157-166
DOI：10.14257/ijsip.2015.8.9.16
出版社：SERSC
摘要：In complicated scene, in order to solve the temporal occlusion problem of target tracking, a novel particle filter tracking algorithm based on graphical model is proposed. Graphical model is applied to particle filtering in this method. Firstly, dividing the target into several key regions, and extracting the characteristic value of each region. Then, these regions are applied to construct graphical model. In the process of target tracking by using particle filter method, graphical models can compensate for the lacking information of the occluded region. The state of the occluded part can be inferred by the graphical model. Finally, experimental results have demonstrated that the proposed tracking algorithm is effective, and it can reliably track moving target.
关键词：Visual tracking; PGM; Marcov chains; Posterior density