Enhanced Online Convolutional Neural Networks for Object Tracking

被引：0

作者：

Zhang, Dengzhuo ^{[1
]}

Gao, Yun ^{[1
,2
]}

Zhou, Hao ^{[1
]}

Li, Tianwen ^{[3
]}

机构：

[1] Yunnan Univ, Sch Informat Sci & Engn, Kunming, Yunnan, Peoples R China

[2] Kunming Inst Phys, Kunming, Yunnan, Peoples R China

[3] Kunming Univ Sci & Technol, Fac Sci, Kunming, Yunnan, Peoples R China

来源：

TENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2017) | 2018年 / 10696卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

object tracking; online convolution neural network; k-means plus; error back-propagation;

D O I：

10.1117/12.2310122

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

引用

页数：7

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