基于孪生神经网络的目标跟踪算法进展研究

doi:10.12305/j.issn.1001-506X.2022.06.06

摘要/Abstract

摘要：

目标跟踪作为计算机视觉领域的关键课题, 广泛应用在智能视频监控等领域。随着深度学习的迅速发展, 基于孪生神经网络的跟踪算法(简称为孪生跟踪算法)因其速度和精度的平衡优势成为了主流算法。尽管已有大量研究, 但仍缺乏从跟踪框架层面对孪生跟踪算法进行系统分析。为了梳理目前孪生跟踪算法的研究进展, 首先介绍了孪生跟踪算法的常见挑战、主要组成、跟踪流程、常用数据集和评价指标; 其次按照对跟踪框架的改进方向分为改进特征提取的算法、优化相似度计算的算法和优化跟踪结果的算法, 并分别详细介绍; 然后对20个主流跟踪算法进行测试与分析; 最后总结目前孪生跟踪算法存在的问题以及对未来的研究方向。

关键词: 目标跟踪, 深度学习, 孪生神经网络, 互相关运算, 区域建议网络

Abstract:

Object tracking, as a key topic in the field of computer vision, is widely used in fields such as intelligent video surveillance. With the rapid development of deep learning, the siamese neural network-based tracking algorithm (referred to as siamese tracking algorithm) becomes the mainstream algorithm due to its balanced advantages of speed and accuracy. Despite a large number of studies, there is still a lack of systematic analysis of siamese tracking algorithms from the level of the tracking framework. In order to sort out the current research progress of siamese tracking algorithms, the common challenges, main components, tracking process, common datasets and evaluation indexes of siamese tracking algorithms are firstly introduced. Secondly, the algorithms are divided into algorithms for improving feature extraction, algorithms for optimizing similarity calculation, and algorithms for optimizing tracking results according to the improvement direction of the tracking framework, and they are introduced in detail respectively. Then 20 mainstream tracking algorithms are tested and analyzed. Finally, we summarize the problems of current siamese tracking algorithms and future research directions.

Key words: object tracking, deep learning, siamese neural network, cross correlation, region proposal network (RPN)

中图分类号:

TP391

金国栋, 薛远亮, 谭力宁, 许剑锟. 基于孪生神经网络的目标跟踪算法进展研究[J]. 系统工程与电子技术, 2022, 44(6): 1805-1822.

Guodong JIN, Yuanliang XUE, Lining TAN, Jiankun XU. Advances in object tracking algorithm based on siamese network[J]. Systems Engineering and Electronics, 2022, 44(6): 1805-1822.

图/表 16

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表1

主要测试集"

数据集	年份	平台	目标类别/个	序列数量/个	平均时长/帧	用途	备注
OTB2013	2013	地面	10	50	578	短时跟踪	-
OTB2015	2015	地面	22	100	590	短时跟踪	-
VOT2013	2013	地面	-	16	-	短时跟踪	-
VOT2014	2014	地面	11	25	416	短时跟踪	多边形标注
VOT2015	2015	地面	-	60	365	短时跟踪	失败时重置机制, 旋转标注
VOT2016	2016	地面	-	60	357	短时跟踪	掩膜分割标注, 包含热成像
VOT2017	2017	地面	24	60	356	短时跟踪	包含热成像
VOT2018	2018	地面	24	60	356	短时跟踪	旋转标注
VOT2018-LT	2018	地面	-	35	420	长时跟踪	数据集为LTB35
VOT2019	2019	地面	30	60	-	短时跟踪	-
VOT2019-LT	2019	地面	-	50	4 305	长时跟踪	数据集为LTB50
VOT2020	2020	地面	30	60	-	短时跟踪	-
VOT2020-LT	2020	地面	-	50	4 305	长时跟踪	数据集为LTB50
ALOV300++	2013	地面	59	314	483	短时跟踪	-
NUS-PRO	2015	地面	12	365	371	短时跟踪	-
TColor-128	2015	地面	27	129	429	短时跟踪	基于颜色信息
TLP	2017	地面	-	50	13 520	长时跟踪	-
Nfs	2017	地面	33	100	3 830	短期跟踪	-
LTB35	2018	地面	-	35	420	长时跟踪	-
LTB50	2018	地面	-	50	4 305	长时跟踪	-
OxUvA	2018	地面	22	366	4 098	长时跟踪	-
TrackingNet(test)	2018	地面	27	511	441	短时+长时跟踪	-
GOT-10k(test)	2019	地面	84	420	-	短时跟踪	-
LaSOT	2019	地面	70	1 400	2 506	长时跟踪	-
Small90/112	2019	地面, 无人机	-	90/112	-	短时跟踪	小目标跟踪数据集
HOB	2020	地面	-	20	2 760	长时跟踪	遮挡数据集
ROB	2021	地面	15	35	286	短时跟踪	旋转处理的数据集
TNL2K	2021	地面	-	2 000	622	短时+长时跟踪	自然语言和边界框双重标签
UAV123	2016	无人机	9	123	915	短时跟踪	-
UAV20L	2016	无人机	5	20	2 934	长时跟踪	-
DTB70	2017	无人机	15	70	225	短时跟踪	-
UAVDT	2018	无人机	-	100	778	短时跟踪	-
VisDrone2019-SOT	2019	无人机	-	132	833	短时跟踪	竞赛数据集
UAVDark135	2021	无人机	-	135	929	短时跟踪	夜间跟踪数据集

表1

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表2

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算法	成功率	准确率	速度/FPS	使用特征
SiamAttn	0.712^①	0.926^①	45	CNN
SiamR-CNN	0.700^②	0.891	5	CNN
SiamCAR	0.697^③	0.910^③	52	CNN
MDNet	0.678	0.909	1	CNN
SiamDWrpn	0.672	0.922^②	13	CNN
SiamRPN++	0.666	0.879	35	CNN
DaSiamRPN	0.658	0.880	160^①	CNN
GradNet	0.639	0.861	80	CNN
DeepSRDCF	0.635	0.851	-	CNN+HOG
SiamRPN	0.629	0.847	160^①	CNN
SiamDWfc	0.627	0.828	93^③	CNN
SRDCF	0.598	0.789	5.6	HOG
CFNet	0.587	0.778	75	CNN
SiamFC	0.587	0.772	58	CNN
Staple	0.578	0.783	80	CH
MEEM	0.539	0.796	10	CN
SAMF	0.535	0.742	7	Gray+HOG+CN
fDSST	0.517	0.686	54.3	HOG+Gray
DSST	0.470	0.693	24	HOG
Struck	0.429	0.584	125^②	Haar+Gray

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