[1]郑凌云,柳培忠,汪鸿翔.结合高斯核函数的卷积神经网络跟踪算法[J].华侨大学学报(自然科学版),2018,39(5):762-767.[doi:10.11830/ISSN.1000-5013.201702123]
　ZHENG Lingyun,LIU Peizhong,WANG Hongxiang.Convolution Neural Networks Tracking Algorithm Combined With Gaussian Kernel Function[J].Journal of Huaqiao University(Natural Science),2018,39(5):762-767.[doi:10.11830/ISSN.1000-5013.201702123]
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结合高斯核函数的卷积神经网络跟踪算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第5期
页码:
762-767
栏目:
出版日期:
2018-09-20

文章信息/Info

Title:
Convolution Neural Networks Tracking Algorithm Combined With Gaussian Kernel Function
文章编号:
1000-5013(2018)05-0762-06
作者:
郑凌云1;  柳培忠2;  汪鸿翔2
1. 华侨大学 后勤与资产管理处, 福建 泉州 362021;2. 华侨大学 工学院, 福建 泉州 362021
Author(s):
ZHENG Lingyun1;  LIU Peizhong2;  WANG Hongxiang2
1. Logistical and Asset Management Office, Huaqiao University, Quanzhou 362021, China; 2. College of Engineering, Huaqiao University, Quanzhou 362021, China
关键词:
视觉跟踪;  卷积神经网络;  高斯核函数;  粒子滤波
Keywords:
visual tracking;  convolutional neural network;  Gauss kernel function;  particle filter
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201702123
文献标志码:
A
摘要:
针对视觉跟踪中运动目标鲁棒性跟踪问题,结合高斯核函数和卷积神经网络(CNN),提出一种无需训练的卷积神经网络提取深度特征的视觉跟踪算法.首先,对初始图像进行归一化处理并聚类提取目标信息,结合跟踪过程中目标信息共同作为卷积网络结构中的各阶滤波器;其次,通过高斯核函数来提高卷积运算速度,提取目标简单抽象特征;最后,通过叠加简单层的卷积结果得到目标的深层次表达,并结合粒子滤波跟踪框架实现跟踪.结果表明:简化后的卷积网络结构能够有效地应对低分辨率、目标遮挡与形变等场景,提高复杂背景下的跟踪效率.
Abstract:
In view of the robustness tracking of moving targets in visual tracking, a vision tracking algorithm is proposed in this paper, which combines Gauss kernel function and convolution neural network(CNN), to extract the depth feature of the convolution neural network without training. Firstly, the initial image is normalized and the target information is extracted by clustering, and the target information in the tracking process is combined as the order filter in the convolution network structure. The Gauss kernel function is used to improve the convolution operation speed, extract the simple abstract feature of the target, and then superimpose the convolution results of the simple layer to get the depth of the target. Finally, we combine particle filter tracking framework to achieve tracking. The results show that the simplified convolution network structure can effectively cope with low resolution, target occlusion and deformation and so on, and improve the tracking efficiency in complex background.

参考文献/References:

[1] 杨戈,刘宏.视觉跟踪算法综述[J].智能系统学报,2010,5(2):95-105.DOI:10.3969/j.issn.1673-4785.2010.02.001.
[2] WU Yi,LIM J,YANG M H.Online object tracking: A benchmark[C]//IEEE Conference on Computer Vision and Pattern Recognition.Portland:IEEE Press,2013:1354-1362.DOI:10.1109/CVPR.2013.312.
[3] BABENKO B,YANG M H,BELONGIE S.Robust object tracking with online multiple instance learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(8):1619-1632.DOI:10.1109/TPAMI.2010.226.
[4] 钟必能,潘胜男.选择性搜索和多深度学习模型融合的目标跟踪[J].华侨大学学报(自然科学版),2016,37(2):207-212.DOI:10.11830/ISSN.1000-5013.2016.02.0207.
[5] MEI Xue,LING Haibin.Robust visual tracking using L1 minimization[C]//IEEE International Conference on Computer Vision.[S.l.]:IEEE Computer Society,2009:1436-1443.DOI:10.1109/ICCV.2009.5459292.
[6] ZHANG Kaihua,ZHANG Lei,YANG Minghsuan.Real-time compressive tracking[C]//Proceedings of the 12th European Conference on Computer Vision-Volume(Ⅲ).Berlin:Springer-Verlag,2012,7574(1):864-877.DOI:10.1007/978-3-642-33712-3_62.
[7] DAVID S,J ROSS,BRUCE A.Visual object tracking using adaptive correlation filters[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Francisco:IEEE Press,2010:2544-2550.DOI:10.1109/CVPR.2010.5539960.
[8] HENRIQUES J F,CASEIRO R,MARTINS P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-596.DOI:10.1109/TPAMI.2014.2345390.
[9] 余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.
[10] WANG Naiyan,YEUNG D Y.Learning a deep compact image representation for visual tracking[C]//27th Annual Conference on Neural Information Processing Systems.Nevada:Curran Associates Inc,2013:809-817.
[11] ZHANG Kaihua,LIU Qingshan,WU Yi,et al.Robust tracking via convolutional networks without learning[J].IEEE Transactions on Image Processing,2015,25(4):1779-1792.
[12] NAM H,HAN B.Learning multi-domain convolutional neural networks for visual tracking[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Press,2016:4293-4302.DOI:10.1109/CVPR.2016.465.
[13] COMAMICIU D,RAMESH V,MEER P.Real-time trackingof non-rigid objects using mean-shift[C]//Proceedings of the IEEE Conference Computer Vision and Patter Recogition.Hilton Head Island:IEEE Press,2000:142-149.DOI:10.1109/CVPR.2000.854761.
[14] ISARD M,BLAKE A.Condensation-conditional density propaga-tion for visual tracking[J].International Journal of Computer Vision,1998,29(1):5-28.DOI:10.1023/A:1008078328650.
[15] 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):125-141.DOI:10.1007/s11263-007-0075-7.
[16] WU Y,LIM J,YANG M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,9(4):2411-2418.DOI:10.1109/TPAMI.2014.2388226.

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备注/Memo

备注/Memo:
收稿日期: 2017-03-28
通信作者: 柳培忠(1976-),男,副教授,博士,主要从事仿生图像处理技术,智能算法的研究.E-mail:pzliu@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61203242); 福建省物联网云计算平台建设基金资助项目(2013H2002); 华侨大学研究生科研创新能力培育计划资助项目(1511422004).
更新日期/Last Update: 2018-09-20