文章基本信息

标题：Visual Object Tracking Robust to Illumination Variation Based on Hyperline Clustering
本地全文：下载
作者：Senquan Yang ; Yuan Xie ; Pu Li 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：1
页码：26-37
DOI：10.3390/info10010026
出版社：MDPI Publishing
摘要：Color histogram-based trackers have obtained excellent performance against many challenging situations. However, since the appearance of color is sensitive to illumination, they tend to achieve lower accuracy when illumination is severely variant throughout a sequence. To overcome this limitation, we propose a novel hyperline clustering based discriminant model, an illumination invariant model that is able to distinguish the object from its surrounding background. Furthermore, we exploit this model and propose an anchor based scale estimation to cope with shape deformation and scale variation. Numerous experiments on recent online tracking benchmark datasets demonstrate that our approach achieve favorable performance compared with several state-of-the-art tracking algorithms. In particular, our approach achieves higher accuracy than comparative methods in the illumination variant and shape deformation challenging situations.
关键词：visual tracking; hyperline clustering; illumination variation; discriminant model; scale estimation visual tracking ; hyperline clustering ; illumination variation ; discriminant model ; scale estimation