Fast Visual Object Tracking via Correlation Filter and Binary Descriptors

被引：0
作者：
Xu, Tianyang [1 ]
Wu, Xiao-Jun [1 ]
Feng, Fei [1 ]
机构：
[1] Jiangnan Univ, Sch IoT Engn, Wuxi 214122, Peoples R China
来源：
2017 INTERNATIONAL SMART CITIES CONFERENCE (ISC2) | 2017年
关键词：
Object Tracking; Correlation Filter; Binary Descriptors; FEATURES; SURF;
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：
Visual object tracking is one of the basic units in the construction of smart cities, which focuses on establishing a dynamic appearance model to represent and recognize the target in complex scenarios. In this paper, we consider visual object tracking as multiple local patches matching problem and design an online tracker based on correlation filter and binary descriptors. We integrate binary descriptors into our tracking model, which provide reliable and robust local feature information. Further, a self-adaptive decision scheme is proposed to fuse the global correlation information and the local feature descriptors. Experimental results on benchmark videos dedicate the effectiveness and robustness of our tracker.
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页数：4
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