基于样本质量估计的空间正则化自适应相关滤波视觉跟踪

侯志强; 王帅; 廖秀峰; 余旺盛; 王姣尧; 陈传华

doi:10.11999/JEIT180921

基于样本质量估计的空间正则化自适应相关滤波视觉跟踪

doi: 10.11999/JEIT180921

侯志强^{1, 2},
王帅^{1, 2, ,},
廖秀峰³,
余旺盛³,
王姣尧³,
陈传华³

1.
西安邮电大学计算机学院西安 710121
2.
西安邮电大学陕西省网络数据分析与智能处理重点实验室西安 710121
3.
空军工程大学电讯工程学院西安 710077

基金项目: 国家自然科学基金(61473309, 61703423)

详细信息

作者简介:
侯志强：男，1973年生，教授，研究方向为计算机视觉和模式识别

王帅：男，1995年生，硕士生，研究方向为计算机视觉和机器学习

廖秀峰：男，1993年生，硕士生，研究方向为计算机视觉和机器学习

余旺盛：男，1985年生，讲师，研究方向为计算机视觉和图像处理

王姣尧：女，1995年生，硕士生，研究方向为计算机视觉和机器学习

陈传华：男，1994年生，硕士生，研究方向为计算机视觉和机器学习

通讯作者:
王帅　2289010261@qq.com

中图分类号: TP391
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出版历程
- 收稿日期: 2018-09-27
- 修回日期: 2019-05-20
- 网络出版日期: 2019-05-27
- 刊出日期: 2019-08-01

Adaptive Regularized Correlation Filters for Visual Tracking Based on Sample Quality Estimation

1.
School of Computer Science & Technology, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
2.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
3.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Funds: The National Natural Science Foundation of China (61473309, 61703423)

摘要

摘要: 相关滤波(CF)方法应用于视觉跟踪领域中效果显著，但是由于边界效应的影响，导致跟踪效果受到限制，针对这一问题，该文提出一种基于样本质量估计的正则化自适应的相关滤波视觉跟踪算法。首先，该算法在滤波器的训练过程中加入空间惩罚项，构建目标与背景的颜色及灰度直方图模板并计算样本质量系数，使得空间正则项根据样本质量系数自适应变化，不同质量的样本受到不同程度的惩罚，减小了边界效应对跟踪的影响；其次，通过对样本质量系数的判定，合理优化跟踪结果及模型更新，提高了跟踪的可靠性和准确性。在OTB2013和OTB2015数据平台上的实验数据表明，与近几年主流的跟踪算法相比，该文算法的成功率均为最高，且与空间正则化相关滤波(SRDCF)算法相比分别提高了9.3%和9.9%。
- 视觉跟踪 /
- 相关滤波 /
- 正则化自适应 /
- 样本质量估计
Abstract: Correlation Filters (CF) are efficient in visual tracking, but their performance is badly affected by boundary effects. Focusing on this problem, the adaptive regularized correlation filters for visual tracking based on sample quality estimation are proposed. Firstly, the proposed algorithm adds spatial regularization matrix to the training process of the filters, and constructs color and gray histogram templates to compute the sample quality factor. Then, the regularization term adaptively changes with the sample quality coefficient, so that the samples of different quality are subject to different degrees of punishment. Then, by thresholding the sample quality coefficient, the tracking results and model update strategy are optimized. The experimental results on OTB2013 and OTB2015 indicate that, compared with the state-of-the-art tracking algorithm, the average success ratio of the proposed algorithm is the highest. The success ratio is raised by 9.3% and 9.9% contrasted with Spatially RegularizeD Correlation Filters(SRDCF) algorithm respectively on OTB2013 and OTB2015.
- Visual tracking /
- Correlation Filters (CF) /
- Adaptive regularization /
- Sample quality estimatio

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图 1 质量系数曲线图

下载: 全尺寸图片幻灯片

图 2 本文算法流程图

下载: 全尺寸图片幻灯片

图 3 8种算法的部分跟踪结果对比

下载: 全尺寸图片幻灯片

图 4 OTB2013和OTB2015数据集上的精度和成功率曲线

下载: 全尺寸图片幻灯片

表 1 自适应正则化的相关滤波视觉跟踪算法

输入：图像序列${{{I}}_1},{{{I}}_2}, ·\!·\!· ,{{{I}}_n}$，目标初始位置${{{p}}_0} = ({x_0},{y_0})$，目标初始尺度${{{s}}_0} = ({w_0},{h_0})$。
输出：每帧图像的跟踪结果，即目标位置${{{p}}_t} = ({x_t},{y_t})$，目标尺度估计${{{s}}_t} = ({w_t},{h_t})$
对于$t = 1,2, ·\!·\!· ,n$, do：
(1) 目标定位及尺度估计
(a) 利用前一帧目标位置${{{p}}_{t - 1}}$以及尺度${{{s}}_{t - 1}}$确定第$t$帧ROI区域；
(b) 提取多尺度样本${{{I}}_s} = \{ {{{I}}_{{s_1}}},{{{I}}_{{s_2}}}, ·\!·\!· {{{I}}_{{s_S}}}\} $；
(c) 根据响应图确定第$t$帧中目标的中心位置${{{p}}_t}$以及尺度${{{s}}_t}$；
(2) 样本质量估计及正则化自适应
(a) 根据目标中心位置及尺度提取目标及背景统计直方图；
(b) 利用式(8)计算样本质量系数$Q$；之后，利用样本质量系数计算空间正则化项；
(3) 模型更新
(a) 利用式(19)更新跟踪滤波器模型${{{ω}}_t}$；
(b) 利用式(17)、式(18)更新统计信息模型${{{h}}_t}$；
结束

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表 2 阈值${τ}$的选取与OTB2015实验结果的对比分析

阈值${\rm{\tau }}$	2500	2750	3000	3250	3500	3750
OTB2015跟踪成功率	0.820	0.779	0.871	0.855	0.817	0.795

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表 3 8组测试序列的中心误差(像素)和成功率(%)

	算法	CNN-SVM	STRCF	TGPR	HCF	KCF	STCT	DSST	C-COT	SMCF
Girl2	7.6(98.0)	11.3(89.0)	30.9(87.0)	110.0(56.0)	118.8(8.0)	264.6(7.0)	319.1(8.0)	46.4(54.0)	8.4(96.0)	7.9(97.0)
Soccer	17.5(81.0)	260(24.0)	19.6(62.0)	60.7(14.0)	13.5(53.0)	15.6(46.0)	46.9(18.0)	14.3(43.0)	12.1(83.0)	14.5(84.0)
Bolt2	6.4(90.0)	151.4(48.0)	7.8(71.0)	304.0(1.0)	8.3(88.0)	329.8(1.0)	6.3(95.0)	115.5(1.0)	7.0(92.0)	6.8(90.0)
KiteSurf	2.3(99.0)	25.2(51.0)	66.7(45.0)	61.7(38.0)	59.8(45.0)	40.6(31.0)	7.8(70.0)	56.7(43.0)	2.1(99.0)	2.3(99.0)
Sylvester	5.5(96.0)	5.0(98.0)	5.5(96.0)	5.7(91.0)	12.9(83.0)	13.3(81.0)	14.8(82.0)	14.8(70.0)	4.5(99.0)	7.5(99.0)
Basketball	3.8(99.0)	21.4(48.0)	14.1(11.0)	9.4(90.0)	3.7(100.0)	8.1(90.0)	3.9(98.0)	111.6(14.0)	5.0(97.0)	4.1(98.0)
Dog1	3.0(100.0)	7.2(58.0)	3.6(100.0)	5.9(69.0)	4.4(67.0)	4.1(64.0)	4.7(97.0)	4.6(66.0)	4.0(98.0)	4.8(96.0)
CarScale	7.4(77.0)	19.8(53.0)	8.7(72.0)	21.4(46.0)	29.3(73.0)	16.1(55.0)	15.2(77.0)	18.8(51.0)	5.3(87.0)	8.7(77.0)
平均	5.8(94.0)	51.7(58.0)	16.2(74.2)	70.6(62.0)	26.6(62.0)	71.3(50.8)	42.5(57.0)	38.9(47.0)	5.4(93.9)	6.1(93.5)

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表 4 不同属性下算法跟踪成功率对此结果

	IV (40)	OPR (64)	SV (66)	OCC (50)	DEF (44)	MB (31)	FM (41)	IPR (31)	OV (14)	BC (33)	LR (10)
本文算法	0.659	0.644	0.640	0.641	0.624	0.672	0.646	0.622	0.600	0.655	0.570
CNN-SVM	0.532	0.546	0.492	0.513	0.547	0.568	0.530	0.545	0.488	0.543	0.419
STRCF	0.646	0.628	0.637	0.618	0.607	0.666	0.634	0.604	0.585	0.639	0.561
TGPR	0.449	0.454	0.400	0.429	0.412	0.409	0.398	0.461	0.373	0.426	0.378
HCF	0.535	0.532	0.487	0.523	0.530	0.573	0.555	0.557	0.474	0.575	0.424
KCF	0.469	0.449	0.399	0.438	0.436	0.456	0.452	0.464	0.393	0.489	0.306
STCT	0.636	0.584	0.596	0.592	0.603	0.625	0.616	0.570	0.530	0.625	0.527
DSST	0.476	0.448	0.414	0.426	0.412	0.465	0.442	0.484	0.374	0.463	0.311
C-COT	0.641	0.637	0.654	0.639	0.637	0.688	0.610	0.635	0.613	0.666	0.583
SMCF	0.672	0.653	0.632	0.653	0.612	0.665	0.632	0.610	0.608	0.663	0.579

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