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
计量
- 文章访问数: 3161
- HTML全文浏览量: 1022
- PDF下载量: 64
- 被引次数: 0
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

HTML全文

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

下载: 导出CSV

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

下载: 导出CSV

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)

下载: 导出CSV

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

下载: 导出CSV

参考文献(25)

SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual tracking: An experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442–1468. doi: 10.1109/TPAMI.2013.230

WANG Naiyan, SHI Jianping, YEUNG D Y, et al. Understanding and diagnosing visual tracking systems[C]. Proceedings of IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3101–3109.

黄立勤, 朱飘. 车载视频下改进的核相关滤波跟踪算法[J]. 电子与信息学报, 2018, 40(8): 1887–1894. doi: 10.11999/JEIT171109

HUANG Liqin and ZHU Piao. Improved kernel correlation filtering tracking for vehicle video[J]. Journal of Electronics &Information Technology, 2018, 40(8): 1887–1894. doi: 10.11999/JEIT171109

BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2544–2550.

HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583–596. doi: 10.1109/TPAMI.2014.2345390

FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627–1645. doi: 10.1109/TPAMI.2009.167

DANELLJAN M, KHAN F S, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 1090–1097.

ZHANG Kaihua, ZHANG Lei, LIU Qingshan, et al. Fast visual tracking via dense spatio-temporal context learning[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 127–141.

MA Chao, HUANG Jiabin, YANG Xiaokang, et al. Hierarchical convolutional features for visual tracking[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3074–2082.

NAM H and HAN B. Learning multi-domain convolutional neural networks for visual tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 4293–4302.

BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]. Computer Vision – ECCV 2016 Workshops, Amsterdam, the Netherlands, 2016: 850–865.

DANELLJAN M, ROBINSON A, KHAN F S, et al. Beyond correlation filters: Learning continuous convolution operators for visual tracking[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 472–488.

MA Chao, YANG Xiaokang, ZHANG Chongyang, et al. Long-term correlation tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 5388–5396.

DANELLJAN M, HAGER G, KHAN F S, et al. Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561–1575. doi: 10.1109/TPAMI.2016.2609928

LI Feng, YAO Yingjie, LI Peihua, et al. Integrating boundary and center correlation filters for visual tracking with aspect ratio variation[C]. IEEE International Conference on Computer Vision Workshops, Venice, Italy, 2017: 2001–2009.

WANG Xin, HOU Zhiqiang, YU Wangsheng, et al. Online scale adaptive visual tracking based on multilayer convolutional features[J]. IEEE Transactions on Cybernetics, 2019, 49(1): 146–158. doi: 10.1109/TCYB.2017.2768570

DANELLJAN M, HÄGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 4310–4318.

LI Feng, TIAN Cheng, ZUO Wangmeng, et al. Learning spatial-temporal regularized correlation filters for visual tracking[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 4904–4913.

毕笃彦, 库涛, 查宇飞, 等. 基于颜色属性直方图的尺度目标跟踪算法研究[J]. 电子与信息学报, 2016, 38(5): 1099–1106. doi: 10.11999/JEIT150921

BI Duyan, KU Tao, ZHA Yufei, et al. Scale-adaptive Object tracking based on color names histogram[J]. Journal of Electronics &Information Technology, 2016, 38(5): 1099–1106. doi: 10.11999/JEIT150921

WU Yi, LIM J, and YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411–2418.

WU Yi, LIM J, and YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848. doi: 10.1109/TPAMI.2014.2388226

MA Chao, HUANG Jiabin, YANG Xiaokang, et al. Hierarchical convolutional features for visual tracking[C]. Proceedings of IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3074–3082.

WANG Lijun, OUYANG Wanli, WANG Xiaogang, et al. STCT: sequentially training convolutional networks for visual tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1373–1381.

HONG S, YOU T, KWAK S, et al. Online tracking by learning discriminative saliency map with convolutional neural network[C]. The 32nd International Conference on Machine Learning, Lille, France, 2015: 597–606.

GAO Jin, LING Haibin, HU Weiming, et al. Transfer learning based visual tracking with Gaussian processes regression[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 188–203.

施引文献

资源附件(0)

访问统计