An Improved Mean Shift Object Tracking Algorithm Based on ORB Feature Matching

被引：0
|
作者：
Yang, Yan [1 ,2 ]
Wang, Xiaodong [1 ,2 ]
Wu, Jiande [1 ,2 ]
Chen, Haitang [1 ,2 ]
Han, Zhaoyuan [1 ,2 ]
机构：
[1] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650500, Peoples R China
[2] Engn Res Ctr Mineral Pipeline Transportat YN, Kunming 650500, Peoples R China
来源：
2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年
关键词：
Mean Shift; Object Tracking; Oriented FAST and Rotated BRIEF; Feature Matching; Robustness;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
It is critical to accurately track objects for video monitoring of intelligent transportation, so an improved Mean Shill object tracking algorithm based on Oriented FAST and Rotated BRIEF (ORB) feature matching was proposed in this paper. The algorithm based on ORB feature matching can be applied to better locate the object in case of great shifts to the tracking window when object is interfered by complex background or rapidly moving. Subsequently, the object location can be accurately tracked through Mean Shift iteration tracking. The experimental results suggested that this algorithm had effectively solved following problems, including poor anti -interference performance and inaccurate tracking of fast moving objects. Meanwhile, it improved the robustness of object tracking algorithms.
引用
收藏
页码：5028 / 5031
页数：4
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