通道稳定性加权补充学习的实时视觉跟踪算法

doi:10.11772/j.issn.1001-9081.2017112735

计算机应用 ›› 2018, Vol. 38 ›› Issue (6): 1751-1754.DOI: 10.11772/j.issn.1001-9081.2017112735

• 虚拟现实与多媒体计算 • 上一篇下一篇

通道稳定性加权补充学习的实时视觉跟踪算法

樊佳庆¹, 宋慧慧², 张开华¹

1. 江苏省大数据分析技术重点实验室(南京信息工程大学), 南京 210044;
2. 南京信息工程大学大气环境与装备技术协同创新中心, 南京 210044

收稿日期:2017-11-20 修回日期:2018-02-24 出版日期:2018-06-10 发布日期:2018-06-13
通讯作者: 张开华
作者简介:樊佳庆(1994-),男,江苏南通人,硕士研究生,主要研究方向:目标跟踪;宋慧慧(1986-),女,山东聊城人,教授,博士,主要研究方向:遥感图像处理;张开华(1983-),男,山东日照人,教授,博士,CCF会员,主要研究方向:图像分割、目标跟踪。
基金资助:
国家自然科学基金资助项目（41501377）；江苏省自然科学基金资助项目（BK20150906，BK20170040）。

Real-time visual tracking algorithm via channel stability weighted complementary learning

FAN Jiaqing¹, SONG Huihui², ZHANG Kaihua¹

1. Jiangsu Key Laboratory of Big Data Analysis Technology(Nanjing University of Information Science and Technology), Nanjing Jiangsu 210044, China;
2. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China

Received:2017-11-20 Revised:2018-02-24 Online:2018-06-10 Published:2018-06-13
Supported by:
This work is partially supported by the National Natural Science Foundation of China (41501377), the Natural Science Foundation of Jiangsu Province (BK20150906, BK20170040).

摘要/Abstract

摘要： 为解决补充学习（Staple）跟踪算法在平面内旋转、部分遮挡时存在的跟踪失败问题，提出了一种通过通道稳定性加权的补充学习（CSStaple）跟踪算法。首先，使用标准相关滤波分类器检测出每层通道的响应值；然后，计算获得每层通道的稳定性权重，并乘到每层权重上，获得相关滤波响应；最后，通过融合颜色补充学习器的响应，得到最终的响应结果，响应中的最大值的位置即为跟踪结果。将所提算法与层和空间可靠性判别相关滤波（CSR-DCF）跟踪、对冲深度跟踪（HDT）、核化相关滤波（KCF）跟踪和Staple等跟踪算法进行了对比实验。实验结果表明，所提算法在成功率上表现最优，在OTB50和OTB100上比Staple分别高出2.5个百分点和0.9个百分点，验证了所提算法对目标在平面内旋转和部分遮挡时的有效性。

Abstract: In order to solve the problem of tracking failure of the Sum of template and pixel-wise learners (Staple) tracking algorithm for in-plane rotation and partial occlusion, a simple and effective Channel Stability-weighted Staple (CSStaple) tracking algorithm was proposed.Firstly, a standard correlation filter classifier was employed to detect the response value of each channel. Then, the stability weight of each channel was calculated and multiplied to the weight of each layer to obtain correlation filtering response. Finally, by integrating the response of the color complementary learner, the final response result was obtained, and the location of the maximum value in the response was the tracking result. The proposed algorithm was compared with several state-of-the-art tracking algorithms including Channel and Spatial Reliability Discriminative Correlation Filter (CSR-DCF) tracking, Hedged Deep Tracking (HDT), Kernelized Correlation Filter (KCF) Tracking and Staple. The experimental results show that, the proposed algorithm performs best in the success rate, it is 2.5 percentage points higher and 0.9 percentage points higher than Staple on OTB50 and OTB100 respectively, which proves the effectiveness of the proposed algorithm for target in-plane rotation and partial occlusion.

Key words: correlation filter, visual tracking, color histogram, complementary learning, channel stability weighting

中图分类号:

TP391.4

樊佳庆, 宋慧慧, 张开华. 通道稳定性加权补充学习的实时视觉跟踪算法[J]. 计算机应用, 2018, 38(6): 1751-1754.

FAN Jiaqing, SONG Huihui, ZHANG Kaihua. Real-time visual tracking algorithm via channel stability weighted complementary learning[J]. Journal of Computer Applications, 2018, 38(6): 1751-1754.

参考文献

[1] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2010:2544-2550.
[2] HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 2012 European Conference on Computer Vision, LNCS 7575. Berlin:Springer, 2012:702-715.
[3] ZHANG K H, ZHANG L, LIU Q S, et al. Fast visual tracking via dense spatio-temporal context learning[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8693. Cham:Springer, 2014:127-141.
[4] 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.
[5] MA C, YANG X K, ZHANG C Y, et al. Long-term correlation tracking[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2015:5388-5396.
[6] BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple:complementary learners for real-time tracking[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2016:1401-1409.
[7] MUELLER M, SMITH N, GHANEM B. Context-aware correlation filter tracking[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:1387-1395.
[8] 朱明敏,胡茂海.基于相关滤波器的长时视觉目标跟踪方法[J].计算机应用,2017,37(5):1466-1470.(ZHU M M, HU M H. Long-term visual object tracking algorithm based on correlation filter[J]. Journal of Computer Applications, 2017, 37(5):1466-1470.)
[9] 彭爽,彭晓明.基于高效多示例学习的目标跟踪[J].计算机应用,2015,35(2):466-469,475.(PENG S. PENG X M. Object tracking with efficient multiple instance learning[J]. Journal of Computer Applications, 2015, 35(2):466-469, 475.)
[10] 李航.统计学习方法[M].北京:清华大学出版社,2012:116.(LI H. Statistical Learning Method[M]. Beijing:Tsinghua University Press, 2012:116.)
[11] WU Y, LIM J, YANG M H. Online object tracking:a benchmark[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2013:2411-2418.
[12] WU Y, LIM J, YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1834-1848.
[13] LUKEZIC A, VOJíR T, ZAJC L C, et al. Discriminative correlation filter with channel and spatial reliability[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:4847-4856.
[14] QI Y K, ZHANG S P, QIN L, et al. Hedged deep tracking[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2016:4303-4311.
[15] NING J F, YANG J M, JIANG S J, et al. Object tracking via dual linear structured SVM and explicit feature map[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2016:4266-4274.

通道稳定性加权补充学习的实时视觉跟踪算法

Real-time visual tracking algorithm via channel stability weighted complementary learning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	姬张建, 任兴旺. 带旋转与尺度估计的全卷积孪生网络目标跟踪算法[J]. 计算机应用, 2021, 41(9): 2705-2711.
[2]	王宁, 宋慧慧, 张开华. 基于距离加权重叠度估计与椭圆拟合优化的精确目标跟踪算法[J]. 计算机应用, 2021, 41(4): 1100-1105.
[3]	曹建芳, 闫敏敏, 贾一鸣, 田晓东. 融合迁移学习的Inception-v3模型在古壁画朝代识别中的应用[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3219-3227.
[4]	熊昌镇, 李言. 动态的加权孪生网络跟踪算法[J]. 计算机应用, 2020, 40(8): 2214-2218.
[5]	张杰, 常天庆, 戴文君, 郭理彬, 张雷. 基于相关滤波与颜色概率模型的目标跟踪算法[J]. 计算机应用, 2020, 40(6): 1774-1782.
[6]	徐小超, 严华. 引入目标分块模型的核相关滤波目标追踪算法[J]. 计算机应用, 2020, 40(3): 683-688.
[7]	费大胜, 宋慧慧, 张开华. 基于多层特征增强的实时视觉跟踪[J]. 计算机应用, 2020, 40(11): 3300-3305.
[8]	李旻择, 李小霞, 王学渊, 孙维. 基于多尺度核特征卷积神经网络的实时人脸表情识别[J]. 计算机应用, 2019, 39(9): 2568-2574.
[9]	贾永超, 何小卫, 郑忠龙. 融合重检测机制的卷积回归网络目标跟踪算法[J]. 计算机应用, 2019, 39(8): 2247-2251.
[10]	杨康, 宋慧慧, 张开华. 基于双重注意力孪生网络的实时视觉跟踪[J]. 计算机应用, 2019, 39(6): 1652-1656.
[11]	杨春德, 刘京, 瞿中. 分离窗口快速尺度自适应目标跟踪算法[J]. 计算机应用, 2019, 39(4): 1145-1149.
[12]	胡秀华, 王长元, 肖锋, 王亚文. 利用空间结构信息的相关滤波目标跟踪算法[J]. 计算机应用, 2019, 39(4): 1150-1156.
[13]	熊晓璇, 王文伟. 基于快速判别式尺度估计的核相关滤波方法[J]. 计算机应用, 2019, 39(2): 546-550.
[14]	闫若怡, 熊丹, 于清华, 肖军浩, 卢惠民. 基于并行跟踪检测框架与深度学习的目标跟踪算法[J]. 计算机应用, 2019, 39(2): 343-347.
[15]	熊昌镇, 车满强, 王润玲. 基于稀疏卷积特征和相关滤波的实时视觉跟踪算法[J]. 计算机应用, 2018, 38(8): 2175-2179.