[1]戴煜彤,陈志国,傅毅.相关滤波的运动目标抗遮挡再跟踪技术[J].智能系统学报,2021,16(4):630-640.[doi:10.11992/tis.202005027]
 DAI Yutong,CHEN Zhiguo,FU Yi.Anti-occlusion retracking technology for a moving target based on correlation filtering[J].CAAI Transactions on Intelligent Systems,2021,16(4):630-640.[doi:10.11992/tis.202005027]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年4期
页码:
630-640
栏目:
学术论文—机器学习
出版日期:
2021-07-05

文章信息/Info

Title:
Anti-occlusion retracking technology for a moving target based on correlation filtering
作者:
戴煜彤1 陈志国1 傅毅2
1. 江南大学 人工智能与计算机学院,江苏 无锡 214122;
2. 无锡环境科学与工程研究中心,江苏 无锡 214153
Author(s):
DAI Yutong1 CHEN Zhiguo1 FU Yi2
1. School of Artificial Intelligence and Computer, Jiangnan University, Wuxi 214122, China;
2. Wuxi Research Center of Environmental Science and Engineering, Wuxi 214153, China
关键词:
目标跟踪相关滤波特征融合ULBP高斯掩码参数峰值均值比卡尔曼预测抗遮挡
Keywords:
object trackingcorrelation filtermulti-feature fusionULBPGaussian maskpeak-to-average ratioKalman predictionanti-occlusion
分类号:
TP391.41
DOI:
10.11992/tis.202005027
摘要:
针对相关滤波在抗遮挡方面效果不佳的问题,本文在ECO_HC(efficient convolution operators handcraft)的基础上提出了一种多特征融合的抗遮挡相关滤波算法。在相关滤波算法的框架下,将目标ULBP(uniform local binary pattern)纹理特征和目标HOG(histogram of oriented gridients)特征进行线性加权融合;在模型建立与更新阶段通过高斯掩码函数缓解循环移位造成的边界效应;通过计算目标最大响应值的峰值均值比来判断目标状态,并将卡尔曼算法作为目标被遮挡后重定位策略。实验结果显示,在16个视频序列上,该文算法的平均精确度达到87.3%,成功率达到76.5%,相比基线算法,分别提升了27.7%和23.7%。
Abstract:
To address the poor anti-occlusion effect of correlation filtering, this paper proposes an anti-occlusion correlation filtering algorithm by means of multifeature fusion based on efficient convolution operators handcraft. First, based on the framework of correlation filtering, a method of linearly weighted fusion is adopted to deal with the target uniform local binary pattern texture feature and the target histogram of oriented gradients feature. Second, the Gaussian mask function is used during the model establishment and update phase to ease the boundary effect caused by cyclic shift. Lastly, the target state is judged by calculating the peak-to-average ratio of the target maximum response value, and the Kalman algorithm is utilized as the relocation strategy after the target is blocked. Experimental results show that the average accuracy of the proposed algorithm reaches 87.3%, and the success rate reaches 76.5% on 16 test sequences, which are 27.7% and 23.7% higher than those of the baseline algorithm, respectively.

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备注/Memo

备注/Memo:
收稿日期:2010-05-21。
基金项目:江苏省高等学校自然科学研究面上项目(17KJB520039);江苏省“333高层次人才培养工程科研项目”(BRA2018147);江苏省高校“青蓝工程”项目(2020年)
作者简介:戴煜彤,硕士研究生,主要研究方向为人工智能与模式识别;陈志国,副教授,IEEE会员,主要研究方向为人工智能、计算机智能控制。参与申报国家科技支撑计划1项、863项目1项,参与教育部科学重大研究项目1项、973军工子项目1项,承担企业研究项目50余项,获得中国轻工业联合会科技进步奖二等奖1项、中国轻工业联合会科技进步奖三等奖1项、无锡市科技进步奖三等奖1项。发表学术论文20余篇;傅毅,副教授,主要研究方向为智能优化算法、生物信息。主持国家自然科学基金青年基金项目1项、江苏省自然科学基金项目1项,参与国家自然科学基金青年基金项目1项、江苏省环境监测科研基金项目1项。发表学术论文30余篇
通讯作者:陈志国.E-mail:427533@qq.com
更新日期/Last Update: 1900-01-01