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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (2): 301-310    DOI: 10.3785/j.issn.1008-973X.2020.02.011
计算机技术、信息工程     
基于各向异性高斯分布的视觉跟踪算法
熊昌镇(),卢颜,闫佳庆
北方工业大学 城市道路交通智能控制技术北京市重点实验室，北京 100144
Visual tracking algorithm based on anisotropic Gaussian distribution
Chang-zhen XIONG(),Yan LU,Jia-qing YAN
Beijing Key Laboratory of Urban Traffic Intelligent Control Technology, North China University of Technology, Beijing 100144, China
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摘要：

为了提高使用传统特征的有效卷积操作算法（ECOhc）的跟踪性能，提出基于各向异性高斯分布的视觉跟踪算法. 该方法根据不同目标的形状比构造水平和垂直方向上带宽不同的各向异性高斯函数，利用该函数训练跟踪器预测目标位置，提高算法的跟踪精度；提取颜色直方图特征跟踪预测新的目标位置，并在决策层加权融合2个预测位置，进一步提高跟踪精度. 在标准数据集OTB-100、VOT2016中测试算法，本研究算法在数据集OTB-100上的平均距离精度为89.6%，平均重叠率为83.7%，比ECOhc算法分别提高4.67%、6.62%；本研究算法在数据集VOT2016上的平均期望重叠率为33.3%，比ECOhc算法提高3.42%. 所提算法能有效提高目标跟踪的精度，在遇到遮挡、光线变化、变形等干扰时仍能稳定跟踪目标.
关键词： 视觉跟踪;  相关滤波;  各向异性高斯分布;  颜色直方图;  加权融合    
Abstract:

A visual tracking algorithm based on the anisotropic Gaussian distribution was proposed, in order to improve the tracking performance of the effective convolution operation algorithm (ECOhc) with traditional features. The anisotropic Gaussian function with different horizontal and vertical bandwidths is constructed according to the shape ratio of different objects and then the function is used to train the tracker so as to predict the position and improve the tracking accuracy. The color histogram features of the object are extracted to track and predict the new position. And then the two predicted positions are weighted fused at the decision layer, which further improves the tracking accuracy. The algorithm was evaluated on the OTB-100 and VOT2016 datasets. The average distance accuracy and overlap rate of the proposed algorithm in OTB-100 were 89.6% and 83.7%, which were 4.67% and 6.62% higher than that of the ECOhc method, respectively. The expected average overlap rate in VOT2016 was 33.3%, which was 3.42% higher than that of the ECOhc method. The proposed algorithm can effectively improve the accuracy of tracking, and it has good robustness when encountering interferences such as occlusion, illumination variation and deformation.
Key words: visual tracking    correlation filter    anisotropic Gaussian distribution    color histogram    weighted fusion
收稿日期: 2019-01-06 出版日期: 2020-03-10
CLC:  TP 491  
基金资助: 国家重点研发计划资助项目（2017YFC0821102）；北京市优秀人才培养资助项目（2017000020124G287）
作者简介: 熊昌镇（1979—），男，副教授，从事深度学习、视频分析研究. orcid.org/0000-0001-7645-5181. E-mail： xczkiong@163.com
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引用本文:

熊昌镇,卢颜,闫佳庆. 基于各向异性高斯分布的视觉跟踪算法[J]. 浙江大学学报(工学版), 2020, 54(2): 301-310.

Chang-zhen XIONG,Yan LU,Jia-qing YAN. Visual tracking algorithm based on anisotropic Gaussian distribution. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 301-310.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.02.011    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I2/301

图 1  本研究所提算法整体框架图
图 2  长宽比不同的2种目标的特征分布图对比
图 3  数据集OTB-100视频中跟踪目标的宽高比和高宽比
图 4  不同位置融合因子下的平均距离精确度
带宽因子组合 $\overline{\rm{DP}} $/ % $\overline{\rm{OP}} $/ %
1）注：第1、2名分别用粗体字和下划线标出
（1/15，1/12） 88.31） 83.2
（1/15，1/11） 88.0 83.1
（1/15，1/10） 89.6 83.7
（1/14，1/13） 87.3 82.0
表 1  不同带宽因子下跟踪算法在数据集OTB-100上的对比结果
跟踪算法 $\overline{\rm{CLE}} $/pixel $\overline{\rm{DP}} $/% $\overline{\rm{OP}} $/% V/（帧·s?1）
1）注：第1、2名分别用粗体字和下划线标出
ECOhc 22.7 85.6 78.5 60.0
ECOhc_sig 19.5 86.7 79.8 69.0
ECOhc_his 18.41） 86.9 82.0 56.1
本研究算法 15.9 89.6 83.7 42.6
表 2  数据集OTB-100上不同算法的对比结果
图 5  4组视频下不同算法的精度曲线和成功率曲线
图 6  数据集OTB-100上10种算法的距离精度和成功率曲线
跟踪算法 $\overline{\rm{DP}} $ / % $\overline{\rm{AUC}} $ / %
1）注：第1、2名分别用粗体字和下划线标出
本研究算法 89.61） 66.5
SiamRPN 85.1 63.7
DaSiamRPN 88.0 65.8
SiamFC+CIR 85.0 64.0
SiamRPN+CIR 86.0 67.0
C-RPN ? 66.3
LDES 76.0 63.4
DAT 89.5 66.8
表 3  近年跟踪算法在数据集OTB-100上的对比结果
图 7  10种算法在典型视频上的跟踪结果
跟踪算法 EAO A R
1）注：第1、2名分别用粗体字和下划线标出
本研究算法 0.333 0.53 1.03
ECO 0.3731） 0.54 0.72
C-COT 0.331 0.52 0.85
ECOhc 0.322 0.53 1.08
CSR-DCF 0.338 0.51 0.85
Staple 0.295 0.54 1.35
D_SRDCF 0.274 0.52 1.23
表 4  不同算法在数据集VOT2016上的对比结果
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