Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)
计算机技术、控制技术     
基于核协同表示的快速目标跟踪算法
王海军, 葛红娟, 张圣燕
1.南京航空航天大学 民航学院,江苏 南京 211106
2.滨州学院 山东省高校航空信息技术重点实验室, 山东 滨州 256603
Fast object tracking algorithm via kernel collaborative presentation
WANG Hai jun, GE Hong juan, ZHANG Sheng yan
1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Key Laboratory of Aviation Information Technology in University of Shandong, Binzhou University, Binzhou 256603, China
 全文: PDF(4247 KB)   HTML
摘要：

针对复杂环境下的单目标跟踪问题,提出一种采用核协同表示的快速目标跟踪算法.在粒子滤波的框架下,在字典矩阵中引入方块矩阵建模跟踪过程中可能出现的遮挡,然后将字典矩阵和候选样本分别映射到高维空间,建立候选样本和字典矩阵在高维空间的线性表示目标跟踪模型,同时采用L2正则化减弱传统方法对系数稀疏性的要求,有效地降低关键步骤的计算复杂度.实验结果表明,该方法能够克服遮挡、光照变化、尺度变化、运动模糊等影响跟踪性能的因素,具有较高的平均覆盖率和较低平均中心点误差,能够实现快速鲁棒的跟踪.
Abstract:
A fast object tracking algorithm via kernel collaborative presentation was proposed to solve the problem of single object tracking in complex scenes. In the framework of particle filter, square matrix was introduced into the dictionary matrix to model the occlusion appeared in the object tracking. Then the dictionary matrix and candidates were mapped to the high dimensional space, separately; the linear representation model between the candidates and dictionary matrix in high space was established. L2 regularization was used to reduce the requirement of the traditional method for the sparsity of coefficients, which can effectively reduce the computational complexity of the key steps. Experimental results show that the proposed algorithm can effectively overcome the influence of occlusion, illumination change, scale change and motion blur, with higher average overlap rate and lower average center location error.
出版日期: 2017-03-06
CLC:  TP 391  
基金资助:

山东省自然科学基金资助项目(ZR2015FL009)；山东省重点研发计划资助项目(2016GGX101023)；山东省高等学校科技计划资助项目(J16LN02)；滨州市科技发展计划资助项目(2013ZC0103)； 滨州学院科研基金资助项目(BZXYG1524,BZXYG1318)
作者简介: 王海军(1980—), 男, 讲师, 博士生, 从事目标跟踪的研究. ORCID: 0000-0003-2481-9662. E-mail: whjlym@163.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　

引用本文:

王海军, 葛红娟, 张圣燕. 基于核协同表示的快速目标跟踪算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2017.02.024.

WANG Hai jun, GE Hong juan, ZHANG Sheng yan. Fast object tracking algorithm via kernel collaborative presentation. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2017.02.024.

［1］ 于慧敏,曾雄.结合排序向量SVM的视频跟踪［J］.浙江大学学报：工学版, 2015, 49(6): 1015-1021.
YU Hui min, ZENG Xiong. Visual tracking combined with ranking vector SVM ［J］. Journal of Zhejiang University： Engineering Science, 2015, 49(6): 1015-1021.
［2］ BABENKO B, YANG M H, BELONGIE S. Robust visual tracking with online multiple instance learning［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33: 1619-1632.
［3］ GAO J, LING H, HU W, et al. Transfer learning based visual tracking with gaussian process regression［C］∥Proceedings of European Conference on Computer Vision. Zurich: Springer, 2014: 188-203.
［4］ MA B, HU H W, SHEN J B, et al. Linearization to Nonlinear Learning for Visual Tracking［C］∥ Proceedings of IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 44004407.
［5］ XUE M, LING H B. Robust visual tracking using L1 minimization［C］∥Proceedings of IEEE International Conference on Computer Vision. Kyoto: IEEE, 2009: 1436-1443.
［6］ BAO C L, WU Y, LING H B, et al. Real time robust L1 tracker using accelerated proximal gradient approach［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012: 1830-1837.
［7］ WANG D, LU H C. On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization［J］. Signal Processing, 2013, 93: 1608-1623.
［8］ JIA X, LU H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012:1830-1837.
［9］ ZHONG W, LU H CH, YANG M H. Robust object tracking via sparse collaborative appearance model［J］. IEEE Transaction on Image Processing, 2014, 23(5): 2356-2368.
［10］ WANG D, LU H CH, XIAO Z Y, el al. Inverse sparse tracker with a locally weighted distance metric［J］. IEEE Transaction on Image Processing, 2015, 24(9): 2646-2657.
［11］ WANG D, LU H, YANG M H. Kernel collaborative face recognition［J］. Pattern Recognition, 2015, 48(10): 3025-3237.
［12］ LI W, DU Q, XIONG M M. Kernel collaborative representation with tikhonov regularization for hyperspectral image classification［J］. IEEE Geoscience and Remote Sensing Letters, 2015, 12(1): 48-52.
［13］ ZHANG L, YANG M, FENG X. Sparse representation or collaborative representation: which helps face recognition？［C］∥Proceedings of IEEE International Conference on Computer Vision. Barcelona: IEEE, 2011, 471-478.
［14］ SHI Q, ERIKSSON A, HENGEL A, et al. Is face recognition really a compressive sensing problem？［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs: IEEE, 2011: 553-560.
［15］ WANG D, LU H C, Yang M H, et al. Least soft-threshold square tracking［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Porland OR: IEEE, 2013: 2371-2378.
［16］ WANG D, LU H C, Yang M H. Online object tracking with sparse prototypes［J］. IEEE Transaction on Image Processing, 2013, 22(1): 314-325.
［17］ ADAM A, RIVLIN E. Robust fragments-based tracking using the integral histogram［C］∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington: IEEE, 2006: 798-805.
［18］ WANG D, LU H CH. Fast and robust object tracking via probability continuous outlier model［J］. IEEE Transaction on Image Processing, 2015, 24(12):5166-5176.
［19］ WANG D, LU H CH. Visual tracking via probability continuous outlier model［C］∥ Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH: IEEE, 2014: 3478-3485.
[1] 郑守国,张勇德,谢文添,樊虎,王青. 基于数字孪生的飞机总装生产线建模[J]. 浙江大学学报(工学版), 2021, 55(5): 843-854.
[2] 张师林,马思明,顾子谦. 基于大边距度量学习的车辆再识别方法[J]. 浙江大学学报(工学版), 2021, 55(5): 948-956.
[3] 宋鹏,杨德东,李畅,郭畅. 整体特征通道识别的自适应孪生网络跟踪算法[J]. 浙江大学学报(工学版), 2021, 55(5): 966-975.
[4] 蔡君,赵罡,于勇,鲍强伟,戴晟. 基于点云和设计模型的仿真模型快速重构方法[J]. 浙江大学学报(工学版), 2021, 55(5): 905-916.
[5] 王虹力,郭斌,刘思聪,刘佳琪,仵允港,於志文. 边端融合的终端情境自适应深度感知模型[J]. 浙江大学学报(工学版), 2021, 55(4): 626-638.
[6] 张腾,蒋鑫龙,陈益强,陈前,米涛免,陈彪. 基于腕部姿态的帕金森病用药后开-关期检测[J]. 浙江大学学报(工学版), 2021, 55(4): 639-647.
[7] 郑英杰,吴松荣,韦若禹,涂振威,廖进,刘东. 基于目标图像FCM算法的地铁定位点匹配及误报排除方法[J]. 浙江大学学报(工学版), 2021, 55(3): 586-593.
[8] 雍子叶,郭继昌,李重仪. 融入注意力机制的弱监督水下图像增强算法[J]. 浙江大学学报(工学版), 2021, 55(3): 555-562.
[9] 于勇,薛静远,戴晟,鲍强伟,赵罡. 机加零件质量预测与工艺参数优化方法[J]. 浙江大学学报(工学版), 2021, 55(3): 441-447.
[10] 胡惠雅,盖绍彦,达飞鹏. 基于生成对抗网络的偏转人脸转正[J]. 浙江大学学报(工学版), 2021, 55(1): 116-123.
[11] 陈杨波,伊国栋,张树有. 基于点云特征对比的曲面翘曲变形检测方法[J]. 浙江大学学报(工学版), 2021, 55(1): 81-88.
[12] 段有康,陈小刚,桂剑,马斌,李顺芬,宋志棠. 基于相位划分的下肢连续运动预测[J]. 浙江大学学报(工学版), 2021, 55(1): 89-95.
[13] 张太恒,梅标,乔磊,杨浩杰,朱伟东. 纹理边界引导的复合材料圆孔检测方法[J]. 浙江大学学报(工学版), 2020, 54(12): 2294-2300.
[14] 梁栋,刘昕宇,潘家兴,孙涵,周文俊,金子俊一. 动态背景下基于自更新像素共现的前景分割[J]. 浙江大学学报(工学版), 2020, 54(12): 2405-2413.
[15] 晋耀,张为. 采用Anchor-Free网络结构的实时火灾检测算法[J]. 浙江大学学报(工学版), 2020, 54(12): 2430-2436.