[1]茅正冲,黄舒伟.基于结构化判别稀疏表示的目标跟踪[J].南京理工大学学报(自然科学版),2018,42(03):271.[doi:10.14177/j.cnki.32-1397n.2018.42.03.003]
 Mao Zhengchong,Huang Shuwei.Structured discriminant sparse representation based object tracking[J].Journal of Nanjing University of Science and Technology,2018,42(03):271.[doi:10.14177/j.cnki.32-1397n.2018.42.03.003]
点击复制

基于结构化判别稀疏表示的目标跟踪
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年03期
页码:
271
栏目:
出版日期:
2018-06-30

文章信息/Info

Title:
Structured discriminant sparse representation based object tracking
文章编号:
1005-9830(2018)03-0271-07
作者:
茅正冲黄舒伟
江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
Author(s):
Mao ZhengchongHuang Shuwei
Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University,Wuxi 214122,China
关键词:
结构化稀疏表示 目标跟踪 尺度不变特征变换 分类器 字典
Keywords:
structured sparse representation object tracking scale-invariant feature transform classifiers dictionaries
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.003
摘要:
针对稀疏表示目标跟踪算法采用整体模板且区分目标与背景的能力差的缺点,该文提出了一种改进算法。采用尺度不变特征变换(SIFT)对目标进行特征提取。采用结构化稀疏表示的外观模型对候选目标进行稀疏表示,得到稀疏系数。通过正负样本设计并训练判别分类器,然后对候选目标进行分类,获得置信值。采用上一帧的跟踪结果对分类器与字典进行更新。对该文算法进行了仿真研究。计算仿真结果中3种测试序列的平均重叠率和平均中心点误差,Deer测试序列的值为0.633 8和9.397 6,Car11测试序列的值为0.677 5和1.943 3,Caviar2测试序列的值为0.753 5和3.838 2。
Abstract:
An improved algorithm is proposed aiming at such shortcomings of sparse representation based object tracking algorithm as using an overall template and the poor ability of distinguishing targets from a background. Scale-invariant feature transform(SIFT)is used to extract the features of a target. Candidate objects are sparsely represented using appearance models of structured sparse representation,and sparse coefficients are obtained. A discriminant classifier is designed and trained by positive and negative samples,candidate objects are classified,and a confidence value is obtained. The tracking result of the previous frame is used to update the classifier and the dictionary. The improved algorithm is simulated. The average overlap ratio and average center point error of 3 test sequences of the simulation results are calculated,and Deer test sequence's are 0.633 8 and 9.397 6,Car11 test sequence's are 0.677 5 and 1.943 3,Caviar2 test sequence's are 0.753 5 and 3.838 2.

参考文献/References:

[1] Shu C F,Hampapur A,Lu M,et al. IBM smart surveillance system(S3):An open and extensible framework for event based surveillance[C]//IEEE Conference on Advanced Video and Signal Based Surveillance. Como,Italy:IEEE,2005:318-323.
[2]Shah M,Javed O,Shafique K. Automated visual surveillance in realistic scenarios[J]. IEEE Multimedia,2007,14(1):30-39.
[3]杜鹃,吴芬芬. 高斯混合模型的运动目标检测与跟踪算法[J]. 南京理工大学学报,2017,41(1):41-46.
Du Juan,Wu Fenfen. Movement target tracking algorithm by using Gaussian mixture model[J]. Journal of Nanjing University of Science and Technology,2017,41(1):41-46.
[4]许敬,王晓锋. 基于贝叶斯概率的运动目标识别方法[J]. 南京理工大学学报,2013,37(1):80-84.
Xu Jing,Wang Xiaofeng. Recognition method of moving target using Bayesian probability theory[J]. Journal of Nanjing University of Science and Technology,2013,37(1):80-84.
[5]Zhang Shengping,Yao Hongxun,Sun Xin,et al. Sparse coding based visual tracking:Review and experimental comparison[J]. Pattern Recognition,2013,46(7):1772-1788.
[6]张少辉,王迤冉. 用于图像识别的稀疏高斯编码[J]. 南京理工大学学报,2016,40(1):61-66.
Zhang Shaohui,Wang Yiran. Sparse Gaussian coding for image recognition[J]. Journal of Nanjing University of Science and Technology,2016,40(1):61-66.
[7]Mei Xue,Ling Haibin. Robust visual tracking using 1 minimization[EB/OL]. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5459292,2018-05-12.
[8]Bai Tianxiang,Li Y F. Robust visual tracking with structured sparse representation appearance model[J]. Pattern Recognition,2012,45(6):2390-2404.
[9]Zhang Qi,Rui Ting,Fang Husheng,et al. Particle filter object tracking based on Harris-SIFT feature matching[J]. Procedia Engineering,2012,29:924-929.
[10]白廷柱,侯喜报. 基于SIFT算子的图像匹配算法研究[J]. 北京理工大学学报,2013,33(6):622-627.
Bai Tingzhu,Hou Xibao. An improved image matching algorithm based on SIFT[J]. Transactions of Beijing Institute of Technology,2013,33(6):622-627.
[11]Babenko B,Yang M H,Belongie S. Visual tracking with online multiple instance learning.[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA:IEEE,2009:983-990.
[12]Ross D A,Lim J,Lin R S,et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision,2008,77(1-3):125-141.

相似文献/References:

[1]程相权,王远钢,郭治.满足滞留度指标的图像波门预测技术研究[J].南京理工大学学报(自然科学版),2001,(04):346.
 ChengXiangquan WangYuangang GuoZhi.Prediction Study of Tracking Window with Specified Residence Index[J].Journal of Nanjing University of Science and Technology,2001,(03):346.
[2]杨春玲,孙泓波,倪晋麟,等.多传感器远距离目标跟踪精度分析[J].南京理工大学学报(自然科学版),1999,(03):53.
 YangChunling SunHongbo NiJinlin LiuGuosui.Research of Multisensor for long Range Target Tracking Accuracy[J].Journal of Nanjing University of Science and Technology,1999,(03):53.
[3]刘华军,赖少发.汽车毫米波雷达目标跟踪的快速平方根CKF算法[J].南京理工大学学报(自然科学版),2016,40(01):56.
 Liu Huajun,Lai Shaofa.Fast square root CKF for automotive millimeter-waveradar target tracking[J].Journal of Nanjing University of Science and Technology,2016,40(03):56.
[4]杜 鹃,吴芬芬.高斯混合模型的运动目标检测与跟踪算法[J].南京理工大学学报(自然科学版),2017,41(01):41.[doi:10.14177/j.cnki.32-1397n.2017.41.01.006]
 Du Juan,Wu Fenfen.Movement target tracking algorithm by using Gaussian mixture model[J].Journal of Nanjing University of Science and Technology,2017,41(03):41.[doi:10.14177/j.cnki.32-1397n.2017.41.01.006]

备注/Memo

备注/Memo:
收稿日期:2017-07-10 修回日期:2017-10-24
基金项目:国家自然科学基金(60973095); 江苏省产学研联合创新资金(BY2015019-29)
作者简介:茅正冲(1964-),男,副教授,主要研究方向:机器人视听觉识别、工业控制,E-mail:1063519780@qq.com。
引文格式:茅正冲,黄舒伟. 基于结构化判别稀疏表示的目标跟踪[J]. 南京理工大学学报,2018,42(3):271-277.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-06-30