Combining Siamese Network and Regression Network for Visual Tracking

被引：2

作者：

Ge, Yao ^{[1
]}

Chen, Rui ^{[2
]}

Tong, Ying ^{[2
]}

Cao, Xuehong ^{[2
]}

Liang, Ruiyu ^{[2
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Peoples R China

[2] Nanjing Inst Technol, Sch Commun Engn, Nanjing, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2020年 / E103D卷 / 08期

关键词：

regression network; siamese network; two-stage; visual tracking; OBJECT TRACKING;

D O I：

10.1587/transinf.2020EDL8032

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We combine the siamese network and the recurrent regression network, proposing a two-stage tracking framework termed as SiamReg. Our method solves the problem that the classic siamese network can not judge the target size precisely and simplifies the procedures of regression in the training and testing process. We perform experiments on three challenging tracking datasets: VOT2016, OTB100, and VOT2018. The results indicate that, after offline trained, SiamReg can obtain a higher expected average overlap measure.

引用

页码：1924 / 1927

页数：4

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