Visual tracking based on transfer learning of deep salience information

In this paper, we propose a new visual tracking method in light of salience information and deep learning. Salience detection is used to exploit features with salient information of the image. Complicated representations of image features can be gained by the function of every layer in convolution n...

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Autores principales:	Zuo Haorui, Xu Zhiyong, Zhang Jianlin, Jia Ge
Formato:	article
Lenguaje:	EN
Publicado:	Institue of Optics and Electronics, Chinese Academy of Sciences 2020
Materias:	convolution neural network transfer learning salience detection visual tracking Optics. Light QC350-467
Acceso en línea:	https://doaj.org/article/e230fac26e2948d28ca973523f087436
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spelling	oai:doaj.org-article:e230fac26e2948d28ca973523f0874362021-11-10T10:02:56ZVisual tracking based on transfer learning of deep salience information2096-457910.29026/oea.2020.190018https://doaj.org/article/e230fac26e2948d28ca973523f0874362020-09-01T00:00:00Zhttp://www.oejournal.org/article/doi/10.29026/oea.2020.190018https://doaj.org/toc/2096-4579In this paper, we propose a new visual tracking method in light of salience information and deep learning. Salience detection is used to exploit features with salient information of the image. Complicated representations of image features can be gained by the function of every layer in convolution neural network (CNN). The characteristic of biology vision in attention-based salience is similar to the neuroscience features of convolution neural network. This motivates us to improve the representation ability of CNN with functions of salience detection. We adopt the fully-convolution networks (FCNs) to perform salience detection. We take parts of the network structure to perform salience extraction, which promotes the classification ability of the model. The network we propose shows great performance in tracking with the salient information. Compared with other excellent algorithms, our algorithm can track the target better in the open tracking datasets. We realize the 0.5592 accuracy on visual object tracking 2015 (VOT15) dataset. For unmanned aerial vehicle 123 (UAV123) dataset, the precision and success rate of our tracker is 0.710 and 0.429.Zuo HaoruiXu ZhiyongZhang JianlinJia GeInstitue of Optics and Electronics, Chinese Academy of Sciencesarticleconvolution neural networktransfer learningsalience detectionvisual trackingOptics. LightQC350-467ENOpto-Electronic Advances, Vol 3, Iss 9, Pp 190018-1-190018-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolution neural network transfer learning salience detection visual tracking Optics. Light QC350-467
spellingShingle	convolution neural network transfer learning salience detection visual tracking Optics. Light QC350-467 Zuo Haorui Xu Zhiyong Zhang Jianlin Jia Ge Visual tracking based on transfer learning of deep salience information
description	In this paper, we propose a new visual tracking method in light of salience information and deep learning. Salience detection is used to exploit features with salient information of the image. Complicated representations of image features can be gained by the function of every layer in convolution neural network (CNN). The characteristic of biology vision in attention-based salience is similar to the neuroscience features of convolution neural network. This motivates us to improve the representation ability of CNN with functions of salience detection. We adopt the fully-convolution networks (FCNs) to perform salience detection. We take parts of the network structure to perform salience extraction, which promotes the classification ability of the model. The network we propose shows great performance in tracking with the salient information. Compared with other excellent algorithms, our algorithm can track the target better in the open tracking datasets. We realize the 0.5592 accuracy on visual object tracking 2015 (VOT15) dataset. For unmanned aerial vehicle 123 (UAV123) dataset, the precision and success rate of our tracker is 0.710 and 0.429.
format	article
author	Zuo Haorui Xu Zhiyong Zhang Jianlin Jia Ge
author_facet	Zuo Haorui Xu Zhiyong Zhang Jianlin Jia Ge
author_sort	Zuo Haorui
title	Visual tracking based on transfer learning of deep salience information
title_short	Visual tracking based on transfer learning of deep salience information
title_full	Visual tracking based on transfer learning of deep salience information
title_fullStr	Visual tracking based on transfer learning of deep salience information
title_full_unstemmed	Visual tracking based on transfer learning of deep salience information
title_sort	visual tracking based on transfer learning of deep salience information
publisher	Institue of Optics and Electronics, Chinese Academy of Sciences
publishDate	2020
url	https://doaj.org/article/e230fac26e2948d28ca973523f087436
work_keys_str_mv	AT zuohaorui visualtrackingbasedontransferlearningofdeepsalienceinformation AT xuzhiyong visualtrackingbasedontransferlearningofdeepsalienceinformation AT zhangjianlin visualtrackingbasedontransferlearningofdeepsalienceinformation AT jiage visualtrackingbasedontransferlearningofdeepsalienceinformation
_version_	1718440149664137216

Visual tracking based on transfer learning of deep salience information

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