基于改进核相关滤波器的目标跟踪算法

doi:10.19678/j.issn.1000-3428.0048918

计算机工程 ›› 2018, Vol. 44 ›› Issue (11): 222-227. doi: 10.19678/j.issn.1000-3428.0048918

基于改进核相关滤波器的目标跟踪算法

江维创,张俊为,桂江生

浙江理工大学信息学院,杭州 310018

收稿日期:2017-10-11 出版日期:2018-11-15 发布日期:2018-11-15
作者简介:江维创(1992—),男,硕士研究生,主研方向为计算机视觉、图像处理、机器学习;张俊为,硕士研究生;桂江生,副教授、博士。
基金资助:
国家自然科学基金(61105035);浙江省重大科技专项重点工业项目(2014C01047)。

Target Tracking Algorithm Based on Improved Kernel Correlation Filter

JIANG Weichuang,ZHANG Junwei,GUI Jiangsheng

School of Informatics and Electronics,Zhejiang Sci-Tech University,Hangzhou 310018,China

Received:2017-10-11 Online:2018-11-15 Published:2018-11-15

摘要/Abstract

摘要：

在跟踪场景中,目标自身和背景会发生不可预测的变化,给目标跟踪带来较大困难。针对该问题,建立一种基于核相关滤波器(KCF)处理遮挡的跟踪算法。根据前向分类器响应最大值的分布特征建立遮挡处理模型,采用阈值方法进行遮挡检测,在目标受到遮挡之后通过块区域螺旋搜索方法进行目标搜索,在目标搜索过程中计算滑动框的响应判定是否为目标。在OTB测试序列集的测试结果表明,与Staple、DSST、KCF、SST算法相比,该算法在跟踪准确度上比次优方法提高6.1%,在跟踪成功率上比次优方法提高1.5%。

Abstract:

In the real tracking scene,the unpredictable changes of the target itself and the background bring great difficulty to the target tracking.This paper proposes a target tracking algorithm based on Kernel Correlation Filter (KCF) for occlusion.The algorithm establishes an occlusion processing model based on the distribution characteristics of the forward classifier response maximum value,and uses the threshold method to perform occlusion detection.After the target is occluded,the target search is performed by the block region helix search method,and the response of the sliding box is calculated in the target search process to determine whether the target is found.The algorithm is tested on the OTB test sequence and compared with the Staple,DSST,KCF,SST algorithms.Results show that the proposed algorithm improves the tracking accuracy by 6.1% compared with the suboptimal algorithm,and increases the tracking success rate by 1.5% compared with the suboptimal algorithm.

Key words: Kernel Correlation Filter (KCF), target tracking, classifier response, occlusion detection, target search

中图分类号:

TP391.4

江维创,张俊为,桂江生. 基于改进核相关滤波器的目标跟踪算法[J]. 计算机工程, 2018, 44(11): 222-227.

JIANG Weichuang,ZHANG Junwei,GUI Jiangsheng. Target Tracking Algorithm Based on Improved Kernel Correlation Filter[J]. Computer Engineering, 2018, 44(11): 222-227.

http://www.ecice06.com/CN/Y2018/V44/I11/222

参考文献

［1］SMEULDERS A W,CHU D M,CUCCHIARA R,et al.Visual tracking:an experimental survey［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,36(7):1442-1468.
［2］黄凯奇,陈晓棠,康运锋,等.智能视频监控技术综述［J］.计算机学报,2015,38(6):1093-1118.
［3］GRABNER H,GRABNER M,BISCHOF H.Real-time tracking via on-line boosting［C］//Proceedings of British Machine Vision Conference.Edinburgh,UK:［s.n.］,2006:47-56.
［4］GRABNER H,LEISTNER C,BISCHOF H.Semi-supervised on-line boosting for robust tracking［C］//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2008:234-247.
［5］KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-learning-detection［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,34(7):1409-1422.
［6］PERNICI F.FaceHugger:the ALIEN tracker applied to faces［J］.Lecture Notes in Computer Science,2012,7585:597-601.
［7］HUA Y,ALAHARI K,SCHMID C.Occlusion and motion reasoning for long-term tracking［M］.Berlin,Germany:Springer,2014:172-187.
［8］III J S S,RAMANAN D.Self-paced learning for long-term tracking［C］//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Computer Society,2013:2379-2386.
［9］王宇霞,赵清杰,蔡艺明,等.基于自重构粒子滤波算法的目标跟踪［J］.计算机学报,2016,39(7):1294-1306.
［10］张凤军,赵岭,安国成,等.一种尺度自适应的Mean Shift跟踪算法［J］.计算机研究与发展,2014,51(1):215-224.
［11］HARE S,SAFFARI A,TORR P H S.Struck:structured output tracking with kernels［C］//Proceedings of International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2011:263-270.
［12］ZHONG W.Robust object tracking via sparsity-based collaborative model［C］//Proceedings of Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2012:1838-1845.
［13］ZHANG K,ZHANG L,YANG M H.Real-time compressive tracking［C］//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2012:864-877.
［14］BABENKO B,YANG M H,BELONGIE S.Visual tracking with online multiple instance learning［C］//Proceedings of IEEE Conference on Computer Vision & Pattern Recognition.Washington D.C.,USA:IEEE Press,2009:983-990.
［15］HENRIQUES J,CASEIRO R,MARTINS P,et al.Exploiting the circulant structure of tracking-by-detection with kernels［M］.Berlin,Germany:Springer,2012:702-715.
［16］BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters［C］//Proceedings of Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press, 2010:2544-2550.
［17］HENRIQUES J F,RUI C,MARTINS P,et al.High-speed tracking with kernelized correlation filters［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(3):583-596.
［18］DALAL N,TRIGGS B.Histograms of oriented gradients for human detection［C］//Proceedings of IEEE Conference on Computer Vision & Pattern Recognition.Washington D.C.,USA:IEEE Press,2005:886-893.
［19］WU Y,LIM J,YANG M H.Online object tracking:a benchmark［C］//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2013:2411-2418.

[1]	王春雷, 张建林, 李美惠, 徐智勇, 魏宇星. 结合卷积Transformer的目标跟踪算法[J]. 计算机工程, 2023, 49(4): 281-288,296.
[2]	周海赟, 项学智, 王馨遥, 任文凯. 多特征融合的端到端链式行人多目标跟踪网络[J]. 计算机工程, 2022, 48(9): 305-313.
[3]	杨帅东, 谌海云, 徐钒诚, 赵书朵, 袁杰敏. 基于孪生区域建议网络的无人机目标跟踪算法[J]. 计算机工程, 2022, 48(1): 288-295,304.
[4]	张海涛, 秦鹏程. 基于GMS与FPME的视频目标跟踪方法[J]. 计算机工程, 2021, 47(7): 226-231.
[5]	任立成, 杨嘉棋, 魏宇星, 张建林. 基于特征融合与双模板嵌套更新的孪生网络跟踪算法[J]. 计算机工程, 2021, 47(7): 239-248.
[6]	苏超群, 朱正为, 郭玉英. 基于高效卷积算子的异常抑制目标跟踪算法[J]. 计算机工程, 2021, 47(7): 266-272,288.
[7]	李志鹏, 张睿. 一种基于对抗学习的实时跟踪模型设计[J]. 计算机工程, 2021, 47(6): 262-270.
[8]	尚桠朝, 孟令军. 基于多特征和尺度估计的KCF_MTSA算法[J]. 计算机工程, 2021, 47(3): 102-108,116.
[9]	罗朔, 侯进, 谭光鸿, 韩雁鹏. 一种低参数的孪生卷积网络实时目标跟踪算法[J]. 计算机工程, 2021, 47(2): 84-89.
[10]	张腾飞, 周书仁, 彭建. 基于双孪生网络的自适应选择跟踪系统[J]. 计算机工程, 2020, 46(6): 103-107.
[11]	沈泽君, 丁飞飞, 杨文元. 多粒度相关滤波视频跟踪方法[J]. 计算机工程, 2020, 46(5): 274-281.
[12]	郗润平, 薛少辉. 基于正交试验的运动目标跟踪算法性能评价[J]. 计算机工程, 2020, 46(3): 254-260,266.
[13]	周双双, 宋慧慧, 张开华, 樊佳庆. 基于增强语义与多注意力机制学习的深度相关跟踪[J]. 计算机工程, 2020, 46(2): 279-285.
[14]	王宇丰, 冯新喜. 基于纯测距的移动传感器网络目标跟踪算法[J]. 计算机工程, 2020, 46(11): 77-83.
[15]	李明杰, 冯有前, 尹忠海, 周诚, 董方昊. 一种用于单目标跟踪的锚框掩码孪生RPN模型[J]. 计算机工程, 2019, 45(9): 216-221.

选择文件类型/文献管理软件名称

选择包含的内容

基于改进核相关滤波器的目标跟踪算法

Target Tracking Algorithm Based on Improved Kernel Correlation Filter

PDF

可视化

被引次数

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

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于改进核相关滤波器的目标跟踪算法

Target Tracking Algorithm Based on Improved Kernel Correlation Filter

PDF

可视化

被引次数

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

本文评价