[1]卢 钢,彭 力.基于置信值重构的视觉目标跟踪算法[J].南京理工大学学报(自然科学版),2018,42(02):210.[doi:10.14177/j.cnki.32-1397n.2018.42.02.012]
 Lu Gang,Peng Li.Visual target tracking algorithm based on confidencevalue reconstruction[J].Journal of Nanjing University of Science and Technology,2018,42(02):210.[doi:10.14177/j.cnki.32-1397n.2018.42.02.012]
点击复制

基于置信值重构的视觉目标跟踪算法()
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02期
页码:
210
栏目:
出版日期:
2018-04-30

文章信息/Info

Title:
Visual target tracking algorithm based on confidencevalue reconstruction
文章编号:
1005-9830(2018)02-0210-07
作者:
卢 钢彭 力
江南大学 物联网工程学院,江苏 无锡 214122
Author(s):
Lu GangPeng Li
School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China
关键词:
置信值重构 局部分块 概率协同表示 局部分块分类 视觉目标跟踪
Keywords:
confidence value reconstruction local patches probabilistic collaborative representation local patch classification visual target tracking
分类号:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2018.42.02.012
摘要:
为了解决视觉目标跟踪过程中出现的目标表观变化和遮挡问题,在粒子滤波框架下,提出一种基于置信值重构的目标跟踪算法。通过对目标模板进行局部分块并结合提取的背景模板构建分类字典,利用概率协同表示分类算法,获得候选目标局部分块的分类概率。然后通过局部分块的分类概率重构候选目标的置信值。最终通过每个候选目标的置信值获得跟踪结果。实验表明,该文算法在目标表观变化和遮挡的情况下能够取得较好的跟踪效果。
Abstract:
In order to solve the problem of deformation and occlusion for the visual target tracking,a target tracking algorithm based on the confidence value reconstruction is proposed under the particle filter framework in this paper. A classification dictionary is constructed by combing the templates of background and the target’s templates using the local patch method. Then,the local patch of the candidates is classified by the probabilistic collaborative representation,and their classification probabilities are further acquired. The confidence value of each candidate is constructed by using the classification probabilities of its local patches. Finally,the tracking result is obtained through the confidence value of all the candidates. Experimental results show that the proposed algorithm is effective for the visual tracking with the deformation and occlusion.

参考文献/References:

[1] Li X,Hu W,Shen C,et al. A survey of appearance models in visual object tracking[J]. ACM Transactions on Intelligent Systems & Technology,2013,4(4):478-488.
[2]Possegger H,Mauthner T,Bischof H. In defense of color-based model-free tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston,US:IEEE,2015:2113-2120.
[3]Bertinetto L,Valmadre J,Golodetz S,et al. Staple:Complementary learners for real-time tracking[J]. Computer Science,2015,38(2):311–323.
[4]王暐,王春平,付强,等. 实时超像素跟踪算法[J]. 电子与信息学报,2016,38(3):571-577.
Wang Wei,Wang Chunping,Fu Qiang,et al. Real-time superpixels based tracking method[J]. Journal of Electronics and Information Technology,2016,38(3):571-577.
[5]Liu T,Wang G,Yang Q. Real-time part-based visual tracking via adaptive correlation filters[C]//Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition. Boston,US:IEEE,2015:4902-4912.
[6]Zarezade A,Rabiee H R,Soltanifarani A,et al. Patchwise joint sparse tracking with occlusion detection[J]. IEEE Transactions on Image Processin,2014,23(10):4496-510.
[7]余旺盛,田孝华,侯志强,等. 基于局部分块学习的在线视觉跟踪[J]. 电子学报,2015,43(1):74-78.
Yu Wangsheng,Tian Xiaohua,Hou Zhiqiang,et al. Online visual tracking based on local patch learning[J]. Chinese Journal of Electronics,2015,43(1):74-78.
[8]侯志强,张浪,余旺盛,等. 基于快速傅里叶变换的局部分块视觉跟踪算法[J]. 电子与信息学报,2015,37(10):2397-2404.
Hou Zhiqiang,Zhang Lang,Yu Wangsheng,et al. Local patch tracking algorithm based on fast Fourier transform[J]. Journal of Electronics and Information Technology,2015,37(10):2397-2404.
[9]Cai S,Zhang L,Zuo W,et al. A probabilistic collaborative representation based approach for pattern classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2016). Seattle,US:IEEE,2016:2950-2959.
[10]Jing L. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision,2008,77(1-3):125-141.
[11]Bao C,Wu Y,Ling H,et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2012. Providence,US:IEEE,2012:1830-1837.
[12]Kwon J,Lee K M. Visual tracking decomposition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2010. San Francisco,US:IEEE,2010:1269-1276.
[13]Zhong W,Lu H,Yang M H. Robust object tracking via sparsity-based collaborative model[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2012. Providence,US:IEEE,2012:1838-1845.
[14]Wu Y,Lim J,Yang M H. Online object tracking:A benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2013. Portland,US:IEEE,2013:2411-2418.

备注/Memo

备注/Memo:
收稿日期:2016-12-05 修回日期:2017-03-03
基金项目:国家自然科学基金(61374047); 江苏省产学研联合创新基金-前瞻性研究项目(BY2014024; BY2014023-36; BY2014023-25)
作者简介:卢钢(1990-),男,硕士生,主要研究方向:视觉跟踪,图像处理,E-mail:1371385143@qq.com; 通讯作者:彭力(1967-),男,博士,教授,主要研究方向:视觉物联网、无线传感网,E-mail:penglimail2002@163.com。
引文格式:卢钢,彭力. 基于置信值重构的视觉目标跟踪算法[J]. 南京理工大学学报,2018,42(2):210-216.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-04-30