Dynamic Update Siamese Networks with Deeper Features

Visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These researches and breakthroughs have made many improvements to solve problems such as drift, lighting, deformation and occlusion. In this paper, we improve the structure of...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 790; no. 1; pp. 12037 - 12044
Main Authors Tan, Jianhao, Tan, Shanshan, Yin, Wang, Liu, Liming, Zhang, Siyuan, Wang, Yaonan
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2020
Subjects
Artificial neural networks
Dynamic update
Feature extraction
Multilayers
Occlusion
Optical tracking
Residual network
Siamese network
Target tracking
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Summary:Visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These researches and breakthroughs have made many improvements to solve problems such as drift, lighting, deformation and occlusion. In this paper, we improve the structure of the AlexNet[1] network by designing the three important influencing factors of the receptive field size, total network step size, and feature filling of the twin network. Apart from this, we add a smoothing matrices and a background suppression matrices to effectively learn the features of the first few frames as much as possible. Fuse multilayer feature elements can learn online about target appearance changes and background suppression, and we train them by using continuous video sequences.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/790/1/012037