基于点轨迹的核相关滤波器跟踪算法

doi:10.11959/j.issn.1000-436x.2018097

通信学报 ›› 2018, Vol. 39 ›› Issue (6): 190-198.doi: 10.11959/j.issn.1000-436x.2018097

• 学术通信 • 上一篇

基于点轨迹的核相关滤波器跟踪算法

吕韵秋,刘凯,程飞

西安电子科技大学计算机学院，陕西西安 710071

修回日期:2018-04-08 出版日期:2018-06-01 发布日期:2018-07-09
作者简介:吕韵秋（1994-），女，江苏徐州人，西安电子科技大学硕士生，主要研究方向为目标跟踪、机器学习。|刘凯（1977-），男，陕西西安人，博士，西安电子科技大学教授，主要研究方向为图像编码、FPGA/嵌入式片上系统。|程飞（1985-），男，陕西宝鸡人，博士，西安电子科技大学讲师，主要研究方向为机器学习、目标跟踪。
基金资助:
国家自然科学基金资助项目(91538101);国家自然科学基金资助项目(61571345)

Kernelized correlation tracking based on point trajectories

Yunqiu LYU,Kai LIU,Fei CHENG

School of Computer Science and Technology,Xidian University,Xi’an 710071,China

Revised:2018-04-08 Online:2018-06-01 Published:2018-07-09
Supported by:
The National Natural Science Foundation of China(91538101);The National Natural Science Foundation of China(61571345)

摘要/Abstract

摘要：

视频跟踪是计算机视觉领域的重要研究方向之一。然而，目标跟踪过程中的遮挡问题会导致视频中目标信息的缺失，因此许多算法无法稳定地跟踪目标。针对遮挡问题，提出一种基于点轨迹的核相关滤波器跟踪算法。通过对目标及其周围物体长期运动轨迹的估计，使用谱聚类对点轨迹进行标记，以区分目标区域与背景区域，从而判断目标是否发生遮挡或漂移，决定是否启用重检测机制对目标进行检测。实验证明，该方法对遮挡和漂移问题具有较强的顽健性。

关键词: 核相关滤波器, 点轨迹, 谱聚类

Abstract:

Visual tracking is one of the most important directions in computer vision.However,many state-of-the-art algorithms cannot track the interested object reliably due to occlusion during tracking process,which leads to deficiency of object information.In order to solve occlusion problem,a kernelized correlation tracking method based on point trajectories was proposed.Through analyzing long-term motion cues of the local information,point trajectories were labeled by spectral clustering.These labeled points were used to differentiate the foreground and background objects and thus detect whether the target was occluded or drifts.If drifting and occlusion occur,re-detection was used to detect the re-entering of the target.Experimental results show that the proposed algorithm can handle occlusion and drifting problems effectively.

Key words: kernelized correlation filter, point trajectories, spectral clustering

中图分类号:

TP391

吕韵秋,刘凯,程飞. 基于点轨迹的核相关滤波器跟踪算法[J]. 通信学报, 2018, 39(6): 190-198.

Yunqiu LYU,Kai LIU,Fei CHENG. Kernelized correlation tracking based on point trajectories[J]. Journal on Communications, 2018, 39(6): 190-198.

图/表 8

图1

图2

图3

图4

表1

表2

图5

图6

参考文献 26

[12]	YAO R , SHI Q , SHEN C ,et al. Part-based visual tracking with online latent structural learning[C]// 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2013: 2363-2370.
[13]	NGUYEN H T , WORRING M , VAN D B R . Occlusion robust adaptive template tracking[C]// 2001 IEEE International Conference on Computer Vision(ICCV). 2001: 678-683.
[14]	WENG S K , KUO C M , TU S K . Video object tracking using adaptive Kalman filter[J]. Journal of Visual Communication and Image Representation, 2006,17(6): 1190-1208.
[15]	BREITENSTEIN M D , REICHLIN F , LEIBE B ,et al. Robust tracking-by-detection using a detector confidence particle filter[C]// 2009 IEEE 12th International Conference on Computer Vision (ICCV). 2009: 1515-1522.
[16]	BROX T , MALIK J . Large displacement optical flow:descriptor matching in variational motion estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(3): 500-513.
[17]	BAKER S , MATTHEWS I . Lucas-kanade 20 years on:a unifying framework[J]. International Journal of Computer Vision, 2004,56(3): 221-255.
[18]	VON LUXBURG U . A tutorial on spectral clustering[J]. Statistics and Computing, 2007,17(4): 395-416.
[19]	BROX T , MALIK J . Object segmentation by long term analysis of point trajectories[C]// European Conference on Computer Vision (ECCV). 2010: 282-295.
[20]	OCHS P , MALIK J , BROX T . Segmentation of moving objects by long term video analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,36(6): 1187-1200.
[21]	WU Y , LIM J , YANG M H . Online object tracking:a benchmark[C]// 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2013: 2411-2418.
[22]	MUELLER M , SMITH N , GHANEM B . A benchmark and simulator for UAV tracking[C]// European Conference on Computer Vision (ECCV). 2016: 445-461.
[23]	PAUWELS K , VAN HULLE M M . Realtime phase-based optical flow on the GPU[C]// 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2008: 1-8.
[24]	KROEGER T , TIMOFTE R , DAI D ,et al. Fast optical flow using dense inverse search[C]// European Conference on Computer Vision (ECCV). 2016: 471-488.
[25]	YAN D , HUANG L , JORDAN M I . Fast approximate spectral clustering[C]// The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009: 907-916.
[1]	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.
[2]	MEI X , LING H . Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(11): 2259-2272.
[3]	LI H , SHEN C , SHI Q . Real-time visual tracking using compressive sensing[C]// 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2011: 1305-1312.
[4]	BABENKO B , YANG M H , BELONGIE S . Visual tracking with online multiple instance learning[C]// 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2009: 983-990.
[5]	KALAL Z , MIKOLAJCZYK K , MATAS J . Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(7): 1409-1422.
[6]	HARE S , GOLODETZ S , SAFFARI A ,et al. Struck:structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016,38(10): 2096-2109.
[7]	BOLME D S , BEVERIDGE J R , DRAPER B A ,et al. Visual object tracking using adaptive correlation filters[C]// 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2010: 2544-2550.
[8]	HENRIQUES J , CASEIRO R , MARTINS P ,et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]// European Conference on Computer Vision (ECCV). 2012: 702-715.
[9]	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.
[10]	SHU G , DEHGHAN A , OREIFEJ O ,et al. Part-based multiple-person tracking with partial occlusion handling[C]// 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2012: 1815-1821.
[11]	YANG B , NEVATIA R . Online learned discriminative part-based appearance models for multi-human tracking[C]// European Conference on Computer Vision (ECCV). 2012: 484-498.
[26]	NAUMOV M , MOON T . Parallel spectral graph partitioning[R]. NVIDIA Technical Report,NVR-2016-001, 2016.

算法	平均成功率	平均准确率（20px）
MCKCF	72.96%	82.63%
KCF	65.75%	68.91%
DFT	53.69%	59.63%
TLD	51.47%	52.80%

算法	Girl	Coke	Jogging	Walking2	Singer1	平均帧率
KCF	298	192	241	122	116	193.8
MCKCF	33	13	10	25	9	17.8

基于点轨迹的核相关滤波器跟踪算法

Kernelized correlation tracking based on point trajectories

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 26

相关文章 10

Metrics

推荐阅读 0

[1]	赵静, 李俊, 龙春, 万巍, 魏金侠, 陈凯. 基于多层次特征的RoQ隐蔽攻击无监督检测方法[J]. 通信学报, 2022, 43(9): 224-239.
[2]	刘真,王娜娜,王晓东,孙永奇. 位置社交网络中谱嵌入增强的兴趣点推荐算法[J]. 通信学报, 2020, 41(3): 197-206.
[3]	蒋伟进, 王扬, 刘晓亮, 吕斯健. 基于词相关性特征的多归属谱聚类突发事件检测[J]. 通信学报, 2020, 41(12): 193-204.
[4]	邱雪松,黄徐川,李文萃,李温静,郭少勇. 面向大规模时间敏感网络的分组调度机制[J]. 通信学报, 2020, 41(11): 124-131.
[5]	房梁,殷丽华,李凤华,方滨兴. 基于谱聚类的访问控制异常权限配置挖掘机制[J]. 通信学报, 2017, 38(12): 63-72.
[6]	周春楠,黄少滨,迟荣华,李雅,郎大鹏. 基于谱聚类的高阶模糊时序自适应预测方法[J]. 通信学报, 2016, 37(2): 107-115.
[7]	覃匡宇,黄传河,王才华,史姣丽,吴笛,陈希. SDN网络中受时延和容量限制的多控制器均衡部署[J]. 通信学报, 2016, 37(11): 90-103.
[8]	相洁,赵冬琴. 改进谱聚类算法在MCI患者检测中的应用研究[J]. 通信学报, 2015, 36(4): 27-34.
[9]	吴健,崔志明,时玉杰,盛胜利,龚声蓉. 基于局部密度构造相似矩阵的谱聚类算法[J]. 通信学报, 2013, 34(3): 14-22.
[10]	徐森,卢志茂,顾国昌. 使用谱聚类算法解决文本聚类集成问题[J]. 通信学报, 2010, 31(6): 0-66.