[1]汪鸿翔,柳培忠,骆炎民,等.高斯核函数卷积神经网络跟踪算法[J].智能系统学报,2018,13(03):388-394.[doi:10.11992/tis.201612040]
 WANG Hongxiang,LIU Peizhong,LUO Yanmin,et al.Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function[J].CAAI Transactions on Intelligent Systems,2018,13(03):388-394.[doi:10.11992/tis.201612040]
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高斯核函数卷积神经网络跟踪算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年03期
页码:
388-394
栏目:
出版日期:
2018-05-05

文章信息/Info

Title:
Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function
作者:
汪鸿翔1 柳培忠1 骆炎民2 杜永兆1 陈智1
1. 华侨大学 工学院, 福建 泉州 362021;
2. 华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
WANG Hongxiang1 LIU Peizhong1 LUO Yanmin2 DU Yongzhao1 CHEN Zhi1
1. College of Engineering, Huaqiao University, Quanzhou 362021, China;
2. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
视觉跟踪深度学习卷积神经网络高斯核函数前景目标背景信息模板匹配粒子滤波
Keywords:
visual trackingdeep learningconvolutional neural network (CNN)gauss kernel functionforeground objectbackground informationtemplate matchingparticle filter
分类号:
TP391
DOI:
10.11992/tis.201612040
摘要:
针对深度学习跟踪算法训练样本缺少、训练费时、算法复杂度高等问题,引入高斯核函数进行加速,提出一种无需训练的简化卷积神经网络跟踪算法。首先,对初始帧目标进行归一化处理并聚类提取一系列初始滤波器组,跟踪过程中结合目标背景信息与前景候选目标进行卷积;然后,提取目标简单抽象特征;最后,将简单层的卷积结果进行叠加得到目标的深层次特征表达。通过高斯核函数加速来提高算法中全部卷积运算的速度,利用目标的局部结构特征信息,对网络各阶段滤波器进行更新,结合粒子滤波跟踪框架实现跟踪。在CVPR2013跟踪数据集上的实验表明,本文方法脱离了繁琐深度学习运行环境,能克服低分辨率下目标局部遮挡与形变等问题,提高复杂背景下的跟踪效率。
Abstract:
In view of such defects existing in the depth learning tracking algorithm as lack of training samples, large time consumption, and high complexity, this paper proposed a simplified convolutional neural network tracking algorithm in which training is unnecessary. Moreover, the Gaussian kernel function can be applied to this algorithm to significantly lower the computing time. Firstly, the initial frame target was normalized and clustered to extract a series of initial filter banks; in the tracking process, the background information of the target and the candidate target for the foreground were convoluted; then the simple and abstract features of the target were extracted; finally, all the convolutions of a simple layer were superposed to form a deep-level feature representation. The Gaussian kernel function was used to speed-up the convolution operations; also, the local structural feature information of the target was used to update the filters in every stage of the network; in addition, the tracking was realized by combining the particle filter tracking framework. The experimental results on the CVPR2013 tracking datasets show that the method used in this paper can help avoid the typically cumbersome operational environment of deep learning, overcome local object occlusion and deformation at low resolution, and improve tracking efficiency under a complex background.

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备注/Memo

备注/Memo:
收稿日期:2016-12-31。
基金项目:国家自然科学基金项目(61203242,61605048);福建省自然科学基金项目(2016J01300,2015J01256);华侨大学研究生科研创新能力培育计划资助项目(1511422004).
作者简介:汪鸿翔,男,1992年生,硕士研究生,主要研究方向为视频、图像处理、视觉跟踪、深度学习相关算法;柳培忠,男,1976年生,副教授,美国杜克大学高级访问学者,博士,主要研究方向为仿生智能计算、仿生图像处理技术、多维空间仿生信息学;骆炎民,男,1975年生,副教授,博士,主要研究方向为智能图像处理、机器学习。
通讯作者:柳培忠.E-mail:pzliu@hqu.edu.cn.
更新日期/Last Update: 2018-06-25