一种新的分段式细粒度正则化的鲁棒跟踪算法

安志勇; 梁顺楷; 李博; 赵峰; 窦全胜; 相忠良

doi:10.16383/j.aas.c220544

一种新的分段式细粒度正则化的鲁棒跟踪算法

doi: 10.16383/j.aas.c220544

安志勇^1,,
梁顺楷^1,,
李博^1,,
赵峰^1,,
窦全胜^1,,
相忠良^1,

1.
山东工商学院计算机科学与技术学院烟台 264003

基金项目: 国家自然科学基金(61976125, 62176140), 山东省自然科学基金(ZR2021MF068, ZR2021MF015, ZR2021MF107, ZR2021QF134)资助

详细信息

作者简介:
安志勇：山东工商学院计算机科学与技术学院副教授. 2008年获得西安电子科技大学计算机系统结构专业博士学位. 主要研究方向为计算机视觉, 目标跟踪. E-mail: azytyut@163.com

梁顺楷：山东工商学院计算机科学与技术学院硕士研究生. 2019年获得广东工业大学物联网工程专业学士学位. 主要研究方向为计算机视觉, 目标跟踪. E-mail: keith1063@163.com

李博：山东工商学院计算机科学与技术学院副教授. 2013年获得东北大学计算机系统结构专业博士学位. 主要研究方向为人工智能, 机器学习. 本文通信作者. E-mail: libokkkkk@sdtbu.edu.cn

赵峰：山东工商学院计算机科学与技术学院教授. 2008年获得西安电子科技大学计算机应用技术专业博士学位. 主要研究方向为人工智能, 机器学习, 医学图像分析和金融大数据分析. E-mail: zhaofeng1016@126.com

窦全胜：山东工商学院计算机科学与技术学院教授. 2005年获得吉林大学计算机应用技术专业博士学位. 主要研究方向为计算智能, 数据挖掘, 知识工程和知识处理. E-mail: douqsh@sdtbu.edu.cn

相忠良：山东工商学院计算机科学与技术学院讲师. 2015年获得韩国东西大学信息技术专业博士学位. 主要研究方向为机器学习, 贝叶斯网络学习. E-mail: zlxiang@sdtbu.edu.cn

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出版历程
- 收稿日期: 2022-07-03
- 录用日期: 2022-11-12
- 网络出版日期: 2023-02-06
- 刊出日期: 2023-05-20

Robust Visual Tracking With a Novel Segmented Fine-grained Regularization

1.
School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264003

Funds: Supported by National Natural Science Foundation of China (61976125, 62176140) and Natural Science Foundation of Shandong Province (ZR2021MF068, ZR2021MF015, ZR2021MF107, ZR2021QF134)

More Information

Author Bio:
AN Zhi-Yong　Associate professor at the School of Computer Science and Technology, Shandong Technology and Business University. He received his Ph.D. degree in computer system architecture from Xidian University in 2008. His research interest covers computer vision and object tracking

LIANG Shun-Kai　Master student at the School of Computer Science and Technology, Shandong Technology and Business University. He received his bachelor degree in internet of things from Guangdong University of Technology in 2019. His research interest covers computer vision and object tracking

LI Bo　Associate professor at the School of Computer Science and Technology, Shandong Technology and Business University. He received his Ph.D. degree in computer system architecture from Northeastern University in 2013. His research interest covers artificial intelligence and machine learning. Corresponding author of this paper

ZHAO Feng　Professor at the School of Computer Science and Technology, Shandong Technology and Business University. He received his Ph.D. degree in computer application technology from Xidian University in 2008. His research interest covers artificial intelligence, machine learning, medical image analysis, and financial big data analysis

DOU Quan-Sheng　Professor at the School of Computer Science and Technology, Shandong Technology and Business University. He received his Ph.D. degree in computer application technology from Jilin University in 2005. His research interest covers computational intelligence, data mining, knowledge engineering, and knowledge management

XIANG Zhong-Liang　Lecturer at the School of Computer Science and Technology, Shandong Technology and Business University. He received his Ph.D. degree in information technology from Dongseo University in 2015. His research interest covers machine learning and Bayesian network learning

摘要

摘要: 孪生网络跟踪算法在训练阶段多数采用$ {L_2}$正则化, 而忽略了网络架构的层次和特点, 因此跟踪的鲁棒性较差. 针对该问题, 提出一种分段式细粒度正则化跟踪(Segmented fine-grained regularization tracking, SFGRT)算法, 将孪生网络的正则化划分为滤波器、通道和神经元三个粒度层次. 创新性地建立了分段式细粒度正则化模型, 分段式可针对不同层次粒度组合, 利用组套索构造惩罚函数, 并通过梯度自平衡优化函数自适应地优化各惩罚函数系数, 该模型可提升网络架构的泛化能力并增强鲁棒性. 最后, 基于VOT2019跟踪数据库的消融实验表明, 与基线算法SiamRPN++比较, 在鲁棒性指标上降低了7.1%及在平均重叠期望(Expected average overlap, EAO)指标上提升了1.7%, 由于鲁棒性指标越小越好, 因此鲁棒性得到显著增强. 基于VOT2018、VOT2019、UAV123和LaSOT等主流数据库的实验也表明, 与国际前沿跟踪算法相比, 所提算法具有较好的鲁棒性和跟踪性能.
- 视觉跟踪 /
- 孪生网络 /
- 细粒度正则化 /
- 组套索
Abstract: Most of the Siamese network tracking algorithms use $ {L_2}$ regularization in the training stage, while ignoring the hierarchy and characteristic of the network architecture. As a result, such trackers have poor robustness. With this insight, we propose a segmented fine-grained regularization tracking (SFGRT) algorithm, which divides the regularization of Siamese network into three fine-grained levels, namely filter level, channel level and shape level. Then we creatively build a segmented fine-grained regularization model that constructs penalty functions based on group lasso, which combines with different levels of granularity to improve generalization ability and robustness. In addition, aiming at the imbalance of gradient magnitude of each penalty function, our approach constructs a gradient self-balancing optimization function to adaptively optimize the coefficients of each penalty function. Finally, ablation study on VOT2019 show that compared with the baseline algorithm SiamRPN++, our approach achieves relative gains of 7.1% and 1.7% in terms of robustness and expected average overlap (EAO) metrics, respectively. It means that the robustness of our tracker is significantly enhanced over baseline tracker since the smaller the robustness metrics, the better. Extensive experiments based on VOT2018, VOT2019, UAV123 and LaSOT show that the proposed algorithm has better robustness and tracking performance than related state-of-the-art methods.
- Visual tracking /
- Siamese network /
- fine-grained regularization /
- group lasso

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图 1 分段式细粒度正则化跟踪算法的训练框架图

Fig. 1 The training framework of the segmented fine-grained regularization tracking

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图 2 细粒度组套索示意图

Fig. 2 Fine-grained group lasso

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图 3 细粒度组套索正则化在各网络分段的效果对比

Fig. 3 Comparison of the effects of fine-grained group lasso regularization in each network segment

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图 4 分段式细粒度正则化模型示意图

Fig. 4 Segmented fine-grained regularization model

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图 5 梯度自平衡优化方法

Fig. 5 The gradient self-balancing optimization approach

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图 6 分段式细粒度正则化和细粒度组套索的跟踪效果对比

Fig. 6 Comparison of tracking results between segmented fine-grained regularization and fine-grained group lasso

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图 7 训练损失曲线对比

Fig. 7 Comparison of training loss curve

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图 8 UAV123基准上的性能对比

Fig. 8 Comparison of tracking results on UAV123

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图 9 LaSOT基准上的性能对比

Fig. 9 Comparison of tracking results on LaSOT

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图 10 UAV123和LaSOT基准上的多挑战属性下精度对比

Fig. 10 Comparison of precision under different challenging attributes on UAV123 and LaSOT benchmarks

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图 11 VOT2019测试集部分视频序列的跟踪结果

Fig. 11 Tracking results for some video sequences on VOT2019

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表 1 VOT2019上的消融实验

Table 1 Ablation study on VOT2019

	基线算法	+细粒度组套索	+分段式细粒度正则化
EAO↑	0.287	0.293	0.304
Accuracy↑	0.595	0.600	0.586
Robustness↓	0.467	0.456	0.396

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表 2 在VOT2018上与SOTA算法的比较

Table 2 Comparison with SOTA trackers on VOT2018

算法	出版	EAO↑	Accuracy↑	Robustness↓
SiamRPN	CVPR2018	0.383	0.586	0.276
SiamRPN++	CVPR2019	0.414	0.600	0.234
SiamMask	CVPR2019	0.380	0.609	0.276
LADCF	ITIP2019	0.389	0.503	0.159
ATOM	CVPR2019	0.401	0.590	0.204
GFS-DCF	ICCV2019	0.397	0.511	0.143
SiamBAN	CVPR2020	0.452	0.597	0.178
SFGRT (Ours)	—	0.422	0.589	0.197

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表 3 在VOT2019上与SOTA算法的比较

Table 3 Comparison with SOTA trackers on VOT2019

算法	出版	EAO↑	Accuracy↑	Robustness↓
SPM	CVPR2019	0.275	0.577	0.507
SiamRPN++	CVPR2019	0.287	0.595	0.467
SiamMask	CVPR2019	0.287	0.594	0.461
SiamDW	CVPR2019	0.299	0.600	0.467
MemDTC	PAMI2019	0.228	0.485	0.587
ATOM	CVPR2019	0.292	0.603	0.411
Roam++	CVPR2020	0.281	0.561	0.438
SiamBAN	CVPR2020	0.327	0.602	0.396
SFGRT (Ours)	—	0.304	0.586	0.396

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表 4 在UAV123基准上与SOTA算法在8个挑战性属性下的精度对比

Table 4 Comparison of precision with SOTA trackers on 8 challenging attributes on UAV123

Attribute	ECO	SiamRPN	DaSiamRPN	SiamRPN++	SiamCAR	SiamBAN	HiFT	SFGRT
Attribute	CVPR2017	CVPR2018	ECCV2018	CVPR2019	CVPR2020	CVPR2020	ICCV2021	—
POC	0.669	0.674	0.701	0.733	0.724	0.765	0.684	0.744
IV	0.710	0.703	0.710	0.775	0.748	0.766	0.700	0.779
CM	0.721	0.778	0.786	0.819	0.797	0.848	0.799	0.838
FM	0.652	0.701	0.737	0.724	0.742	0.805	0.778	0.774
SV	0.707	0.739	0.754	0.780	0.791	0.813	0.768	0.806
BC	0.624	0.589	0.670	0.633	0.659	0.645	0.594	0.651
OV	0.590	0.638	0.693	0.789	0.735	0.789	0.700	0.778
LR	0.683	0.648	0.663	0.658	0.693	0.719	0.655	0.699
Overall	0.741	0.768	0.781	0.804	0.813	0.833	0.787	0.828

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表 5 在LaSOT基准上与SOTA算法在8个挑战性属性下的归一化精度对比

Table 5 Comparison of norm precision with SOTA trackers on 8 challenging attributes on LaSOT

Attribute	SPLT	C-RPN	SiamDW	SiamMask	SiamRPN++	GFS-DCF	ATOM	SiamBAN	CLNet	SFGRT
Attribute	ICCV2019	CVPR2019	CVPR2019	CVPR2019	CVPR2019	ICCV2019	CVPR2019	CVPR2020	ICCV2021	—
DEF	0.520	0.578	0.500	0.593	0.604	0.436	0.574	0.609	0.606	0.620
VC	0.505	0.491	0.350	0.499	0.502	0.427	0.493	0.526	0.494	0.531
IV	0.524	0.603	0.436	0.625	0.633	0.581	0.560	0.642	0.640	0.678
MB	0.465	0.486	0.412	0.493	0.510	0.443	0.564	0.556	0.508	0.557
ROT	0.488	0.520	0.418	0.534	0.552	0.425	0.524	0.579	0.555	0.583
ARC	0.473	0.518	0.415	0.524	0.539	0.423	0.544	0.567	0.546	0.567
SV	0.496	0.540	0.433	0.548	0.568	0.447	0.563	0.595	0.572	0.589
OV	0.447	0.438	0.368	0.458	0.474	0.372	0.473	0.495	0.471	0.507
Overall	0.494	0.542	0.437	0.552	0.570	0.453	0.570	0.598	0.574	0.590

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表 6 不同跟踪算法的模型大小和平均帧速率对比

Table 6 Comparison of model size and average framerate for different trackers

算法	出版	模型大小(MB)	帧速率(FPS)
SiamRPN++	CVPR2019	431.2	80.20
SiamMask	CVPR2019	86.1	106.43
SiamBAN	CVPR2020	430.9	81.76
SFGRT (Ours)	—	431.2	79.99

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