[1]孙海宇,陈秀宏,肖汉雄.联合外形响应的深度目标追踪器[J].智能系统学报,2019,14(04):725-732.[doi:10.11992/tis.201807029]
 SUN Haiyu,CHEN Xiuhong,XIAO Hanxiong.A deep object tracker with outline response map[J].CAAI Transactions on Intelligent Systems,2019,14(04):725-732.[doi:10.11992/tis.201807029]
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联合外形响应的深度目标追踪器(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年04期
页码:
725-732
栏目:
出版日期:
2019-07-02

文章信息/Info

Title:
A deep object tracker with outline response map
作者:
孙海宇 陈秀宏 肖汉雄
江南大学 数字媒体学院, 江苏 无锡 214122
Author(s):
SUN Haiyu CHEN Xiuhong XIAO Hanxiong
School of Digital Media, Jiangnan University, Wuxi 214122, China
关键词:
目标追踪神经网络卷积特征相关滤波位置响应外形信息噪声抑制修正深度学习
Keywords:
object trackingneural networkconvolutional featurescorrelation filterposition responseoutline informationnoise suppressionrectifydeep learning
分类号:
TP391
DOI:
10.11992/tis.201807029
摘要:
针对追踪器使用卷积网络提取出来的特征模板进行目标位置匹配时,易产生响应噪声的问题,本文提出一种联合外形响应和卷积响应的深度目标追踪方法。在当前帧中,由前一帧提供的目标信息先分别提取卷积特征和外形信息,然后获得相应的卷积位置响应和外形位置响应;最后利用外形位置响应对卷积位置响应进行修正,从而有效地抑制响应噪声。实验表明:这种方法具有较高的位置精度,能够提高目标跟踪的准确性。
Abstract:
When convolutional neural network is used as a template to locate target, noise may be unavoidable in the final location response. To solve this problem, we developed a deep object tracker by combining the convolutional position response with the outline position response. For example, in the current frame, after extracting convolutional features and the outline information from the predicted target in the previous frame, we obtained the corresponding convolutional position response and the outline position response, and the latter was used to rectify the former in controlling the noise generated in the convolutional position response. The favorable results of our deep tracker on the benchmark show that the method of integrating the outline position response into the convolutional position response can greatly improve the precision and accuracy of the tracker.

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备注/Memo

备注/Memo:
收稿日期:2018-07-26。
基金项目:江苏省研究生科研与实践创新计划项目(1232050205185680)
作者简介:孙海宇,男,1993年生,硕士研究生,主要研究方向为图像处理、目标跟踪、深度学习相关算法;陈秀宏,男,1964年生,教授,博士后,主要研究方向为数字图像处理和模式识别、目标检测与跟踪、优化理论与方法。发表学术论文100余篇;肖汉雄,男,1991年生,硕士研究生,主要研究方向为模式识别和数字图像处理、人脸识别、深度学习相关算法。
通讯作者:孙海宇.E-mail:6161610009@vip.jiangnan.edu.cn
更新日期/Last Update: 2019-08-25