基于视觉的目标检测与跟踪综述

尹宏鹏; 陈波; 柴毅; 刘兆栋

doi:10.16383/j.aas.2016.c150823

基于视觉的目标检测与跟踪综述

doi: 10.16383/j.aas.2016.c150823

尹宏鹏^1,2, ,,
陈波^2,,
柴毅^2,,
刘兆栋^2,

1.
信息物理社会可信服务计算教育部重点实验室(重庆大学) 重庆 400030
2.
重庆大学自动化学院重庆 400044

基金项目:

重庆市基础科学与前沿研究技术专项重点项目 cstc2015jcyjB0569

中央高校基本科研业务专项基金 106112015CDJXY170003

国家自然科学基金 61203321

重庆市研究生科研创新项目 CYB14023

中央高校基本科研业务专项基金 106112016CDJZR175511

详细信息

作者简介:
陈波  重庆大学自动化学院硕士研究生.2015年获得重庆大学学士学位.主要研究方向为深度学习, 计算机视觉.E-mail:qiurenbieyuan@gmail.com

柴毅  重庆大学自动化学院教授.2001年获得重庆大学博士学位.主要研究方向为信息处理, 融合与控制, 计算机网络与系统控制.E-mail:chaiyi@cqu.edu.cn

刘兆栋  重庆大学自动化学院博士研究生.主要研究方向为稀疏表示, 机器学习.E-mail:liuzhaodong@cqu.edu.cn

通讯作者:
尹宏鹏重庆大学自动化学院副教授.2009年获得重庆大学博士学位.主要研究方向为模式识别, 图像处理与计算机视觉.本文通信作者.E-mail:yinhongpeng@gmail.com

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出版历程
- 收稿日期: 2015-12-14
- 录用日期: 2016-05-16
- 刊出日期: 2016-10-20

Vision-based Object Detection and Tracking: A Review

YIN Hong-Peng^{1,2
, ,},
CHEN Bo^2
,,
CHAI Yi^2
,,
LIU Zhao-Dong^2
,

1.
Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University), Ministry of Education, Chongqing 400030
2.
College of Automation, Chongqing University, Chongqing 400044

Funds:

Chongqing Nature Science Foundation of Fundamental Science and Frontier Technologies cstc2015jcyjB0569

China Central Universities Foundation 106112015CDJXY170003

National Natural Science Foundation of China 61203321

Chongqing Graduate Student Research Innovation Project CYB14023

China Central Universities Foundation 106112016CDJZR175511

More Information

Author Bio:
Master student at the College of Automation, Chongqing University. He received his bachelor0s degree from Chongqing University in 2015. His research interest covers deep learning and computer vision.E-mail:

Professor at the College of Automation, Chongqing University. He received his Ph. D. degree from Chongqing University in 2001. His research interest covers information processing, integration and control, and computer network and system control.E-mail:

Ph. D. candidate at the College of Automation, Chongqing University. His research interest covers sparse representation and machine learning.E-mail:

Corresponding author: YIN Hong-Peng Associate professor at the College of Automation, Chongqing University. He received his Ph. D. degree from Chongqing University in 2009. His research interest covers pattern recognition, image processing, and computer vision. Corresponding author of this paper.E-mail:yinhongpeng@gmail.com

摘要

摘要: 基于视觉的目标检测与跟踪是图像处理、计算机视觉、模式识别等众多学科的交叉研究课题，在视频监控、虚拟现实、人机交互、自主导航等领域，具有重要的理论研究意义和实际应用价值.本文对目标检测与跟踪的发展历史、研究现状以及典型方法给出了较为全面的梳理和总结.首先，根据所处理的数据对象的不同，将目标检测分为基于背景建模和基于前景建模的方法，并分别对背景建模与特征表达方法进行了归纳总结.其次，根据跟踪过程有无目标检测的参与，将跟踪方法分为生成式与判别式，对基于统计的表观建模方法进行了归纳总结.然后，对典型算法的优缺点进行了梳理与分析，并给出了其在标准数据集上的性能对比.最后，总结了该领域待解决的难点问题，对其未来的发展趋势进行了展望.
- 计算机视觉 /
- 目标检测 /
- 目标跟踪 /
- 背景建模 /
- 表观建模
Abstract: Vision-based object detection and tracking is an active research topic in image processing, computer vision, pattern recognition, etc. It finds wide applications in video surveillance, virtual reality, human-computer interaction, autonomous navigation, etc. This survey gives a detail overview of the history, the state-of-the-art, and typical methods in this domain. Firstly, object detection is divided into background-modeling-based methods and foreground-modeling-based methods according to the different data objects processed. Background modeling and feature representation are further summarized respectively. Then, object tracking is divided into generative and discriminative methods according to whether the detection process is involved. Statistical based appearance modeling is presented. Besides, discussions are presented on the advantages and drawbacks of typical algorithms. The performances of different algorithms on benchmark datasets are given. Finally, the outstanding issues are summarized. The future trends of this field are discussed.
- Computer vision /
- object detection /
- object tracking /
- background modeling /
- appearance modeling

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图 1 基于视觉的目标检测与跟踪框架

Fig. 1 General framework of vision-based object detection and tracking

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图 2 基于背景建模的目标检测流程图

Fig. 2 Flow chart of object detection based on background modeling

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图 3 基于目标建模的目标检测流程图

Fig. 3 Flow chart of object detection based on object modeling

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图 4 限制玻尔兹曼机

Fig. 4 Restricted Boltzmann machine

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图 5 基于自编码机的特征表达

Fig. 5 Feature representation based on auto-encoder

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图 6 单层卷积神经网络

Fig. 6 Single layer convolutional neural network

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图 7 基于单层卷积神经网络的特征表达

Fig. 7 Feature representation based on single layer CNN

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图 8 运动目标跟踪一般流程

Fig. 8 Flow chart of moving object tracking

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表 1 基于视觉的目标检测与跟踪应用领域

Table 1 Applications of vision-based object detection and tracking

应用领域	具体应用
智能监控	公共安全监控(犯罪预防、人流密度检测)、停车场、超市、百货公司、自动售货机、ATM、小区(外来人员访问控制)、交通场景、家庭环境(老幼看护)等
虚拟现实	交互式虚拟世界、游戏控制、虚拟工作室、角色动画、远程会议等
高级人机交互	手语翻译、基于手势的控制、高噪声环境(机场、工厂等)下的信息传递等
动作分析	基于内容的运动视频检索, 高尔夫、网球等的个性化训练, 舞蹈等的编排, 骨科患者的临床研究等
自主导航	车辆导航、机器人导航、太空探测器的导航等
机器人视觉	工业机器人、家庭服务机器人、餐厅服务机器人、太空探测器等

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表 2 目标检测与跟踪相关综述文献

Table 2 Related surveys about object detection and tracking

文献	题目	主要内容	讨论主题	发表年限	不足之处
[8]	Vision based hand gesture recognition for human computer interaction: a survey	从检测、跟踪与识别三方面对手势识别的发展现状进行了梳理与总结	检测、跟踪、识别	2015	只进行了某些具体应用方向上的梳理
[9]	A survey on recent object detection techniques useful for monocular vision-based planetary terrain classification	对行星地形分类中的目标检测技术进行了总结	目标检测	2014	只进行了某些具体应用方向上的梳理
[10]	Sparse coding based visual tracking: review and experimental comparison	对基于稀疏编码的目标跟踪进行了全面的梳理与总结, 给出了实验对比与分析	表观建模	2013	只讨论了目标检测与跟踪的组成部分
[11]	A survey of appearance models in visual object tracking	从全局与局部信息描述的角度探讨了目标跟踪中的视觉表达问题	表观建模	2013
[12]	面向目标检测的稀疏表示方法研究进展	综述了稀疏表示方法在目标检测领域中的国内外重要研究进展	表观建模	2015
[13]	Background subtraction techniques: a review	对几种常用的背景减除方法进行了总结	背景建模	2004
[14]	Traditional and recent approaches in background modeling for foreground detection: an overview	对目标检测中背景建模方法进行了详细讨论	背景建模	2014
[15]	Visual tracking: an experimental survey	对19种先进的跟踪器在315段视频序列上进行了对比实验与性能评估	目标跟踪	2014
[16]	Automated human behavior analysis from surveillance videos: a survey	在人体行为理解的底层处理部分, 对目标检测、分类及其跟踪进行了详细阐述	人体行为理解	2014	没有展开讨论检测跟踪问题
[17]	智能视频监控技术综述	在智能视频监控的底层部分, 对目标检测与跟踪进行了讨论	智能监控	2015	没有展开讨论检测跟踪问题
[18]	Object tracking: a survey	对目标跟踪中的目标表达、特征或运动模型选取等问题进行了分类归纳	目标跟踪	2006	发表年限比较久远, 不断更新的理论和方法亟需梳理总结
[19]	视觉跟踪技术综述	分类归纳了视觉跟踪, 并论述了其在视频监控、图像压缩和三维重构等的应用	目标跟踪	2006
[20]	运动目标检测算法的探讨	对2007年以前的主流运动目标检测方法进行了分类讨论	目标检测	2006
[21]	运动目标跟踪算法研究综述	将运动目标跟踪问题分为运动检测与目标跟踪, 并对跟踪算法进行了综述工作	目标跟踪	2009
[22]	微弱运动目标的检测与跟踪识别算法研究	对强噪声背景下的微弱运动目标检测与跟踪算法进行了探讨	目标检测与跟踪	2010

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表 3 基于人工设计的特征表达方法

Table 3 Human-engineering-based feature representation methods

序号	文献	典型算法	主要思想	提出年限	方法类别
1	[4]	SIFT	通过获取特定关键点附近的梯度信息来描述运动目标, 具有旋转、尺度不变等优良特性, 其改进特征主要有PCA-SIFT^[49]、GLOH^[50]、SURF^[51]、DAISY^[52]等	2004	梯度特征
2	[5]	HOG	通过计算空间分布区域的梯度强度及其方向信息来描述运动目标, 其改进特征主要有v-HOG^[53]、CoHOG^[54]、GIST^[55]等	2005	梯度特征
3	[56]	Gabor	利用Gabor滤波器对图像卷积得到, 在一定程度上模拟了人类视觉的细胞感受野机制	1997	模式特征
4	[57]	LBP	通过计算像素点与周围像素的对比信息, 获得的一种对光照不变的局部描述, 其改进特征主要有CS-LBP^[58]、NR-LBP^[59]等	2004
5	[60]	Haar-like	通过计算相邻矩形区域的像素和之差来描述线性、边缘、中心点以及对角线特征, 其改进特征主要有LAB^[66]等	2001
6	[6]	DPM	其实质是一种弹性形状模型, 是通过将梯度直方图(HOG)特征与Latent SVM相结合而训练得到的一种目标形状描述模型	2010	形状特征
7	[69]	Shape context	通过获取形状上某一参考点与其余点的距离分布来描述目标轮廓	2002
8	[71]	kAS	使用一组近似线性的线段对目标形状进行描述, 具有平移、尺度等不变特性	2008
9	[77]	Color names	通过将图像像素值映射至相应的语义属性来对目标进行描述, 该特征通常包含11种语义属性, 一般需要结合梯度特征一起使用	2009	颜色特征
10	[88]	基于熵的显著性特征	通过计算图像像素的灰度概率分布来获取目标的感兴趣区域	2004	颜色特征

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表 4 基于学习的特征表达方法

Table 4 Learning-based feature representation methods

类别	方法名称
基于深度学习的特征表达	CDBN^[102], SBM^[104], DeCAF^[112], R-CNN^[113], SPPNet^[114], Fast R-CNN^[115], Faster R-CNN^[116], segDeepM^[117], MatchNet^[118], OverFe-at^[121], NIN^[122], GoogLeNet^[123], VGGNet^[124], DeepID Net^[125], Vox-Net^[126], SuperCNN^[127], MDNet^[128], DeepSRDCF^[129], SODLT^[130]

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表 5 目标检测典型数据集

Table 5 Typical data sets for object detection

序号	参考文献	数据集名字	数据规模	是否标注	特点及描述	主页链接	发布时间
1	[139]	MIT CBCL Pedestrian Database	共924张图片, 64 × 128, PPM格式	否	人体目标处于图像正中间, 且图像视角限定为正向或背向	http://cbcl.mit.edu/software-datasets/PedestrianData.html	2000
2	[140-141]	USC Pedestrian Detection Test Set	共359张图片, 816个人	是	包含单视角下无遮挡、部分遮挡以及多视角下无遮挡的行人检测数据	http://iris.usc.edu/Vision-Users/OldUsers/bowu/DatasetWebpage/dataset.html	2005 / 2007
3	[5]	INRIA Person Dataset	共1 805张图片, 64 × 128	是	包含了各种各样的应用背景, 对行人的姿势没有特别的要求	http://pascal.inrialpes.fr/data/human/	2005
4	[45, 142]	ChangeDetection.Net	共51段视频, 约140 000帧图片	是	包含了动态背景、目标运动、夜晚及阴影影响等多种挑战	http://changedetection.net/	2012/2014
5	[143]	Caltech Pedestrian Dataset	10小时视频, 640 × 480	是	视频为城市交通环境下驱车拍摄所得, 行人之间存在一定的遮挡	http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/	2009
6	[144]	CVC Datasets	共9个数据集	部分标注	提供了多种应用场景, 如城市、红外等场景, 行人间存在部分遮挡	http://www.cvc.uab.es/adas/site/?q=node/7	2007/2010/2013～2015
7	[119]	PASCAL VOC Datasets	共11540张图, 含20个类	是	该比赛包括分类、检测、分割、动作分类以及人体布局检测等任务	http://host.robots.ox.ac.uk/pascal/VOC/	2005～2012
8	[120]	ImageNet	共14197122张图片	是	大规模目标识别比赛, 包括目标检测、定位以及场景分类等任务	http://image-net.org/	2010～2015
9	[145]	Microsoft COCO	约328000张图片, 含91个类	是	自然场景下的图像分类、检测、场景理解等, 不仅标注了不同的类别, 还对类中个例进行了标注	http://mscoco.org/	2014

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表 6 目标跟踪典型数据集

Table 6 Typical data sets for object tracking

序号	参考文献	数据集	数据规模	是否标注	特点及描述	主页链接	发布时间
1	[209-210]	Visual Tracker Benchmark	100段序列	是	来源于现有文献, 包括了光照及尺度变化、遮挡、形变等9种挑战	http://www.visual-tracking.net	2013
2	[211]	VIVID	9段序列	是	主要任务为航拍视角下的车辆目标跟踪, 具有表观微小、相似等特点	http://vision.cse.psu.edu/data/vividEval/datasets/datasets.html	2005
3	[212]	CAVIAR	28段序列	是	主要用于人体目标跟踪, 视频内容包含行走、会面、进出场景等行为	http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/	2003 / 2004
4	[213]	BIWI Walking Pedestrians Dataset	1段序列	是	主要任务为鸟瞰视角下的行人跟踪, 可用于评测多目标跟踪算法	http://www.vision.ee.ethz.ch/datasets/	2009
5	[214]	“Central” Pedestrian Crossing Sequences	3段序列	是	行人过街序列, 每4帧标定一次	http://www.vision.ee.ethz.ch/datasets/	2007
6	[215]	MOT16	14段序列	是	无约束环境的多目标跟踪, 有不同视角、相机运动、天气影响等挑战	http://motchallenge.net/	2016
7	[216]	PETS2015	7段序列	是	关于停车场中车辆旁边不同活动序列, 可用于目标检测与跟踪、动作识别、场景分析等	http://www.pets2015.net/	2015
8	[217]	VOT Challenge	60段序列(2015年)	是	主要用于短视频跟踪算法的评测, 该比赛从2013年开始举办	http://votchallenge.net/	2013～2015

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表 7 典型跟踪算法的性能对比

Table 7 Performance comparison of typical tracking algorithms

序号	参考文献	跟踪器	准确度	平均失败数	平均覆盖率	速度(EFO)	时间	方法类别
1	[128]	MDNet	0.60	0.69	0.38	0.87	2015	CNN
2	[129]	DeepSRDCF	0.56	1.05	0.32	0.38	2015
3	[130]	SODLT	0.56	1.78	0.23	0.83	2015
4	[218]	SumShift	0.52	1.68	0.23	*16.78*	2011	核学习
5	[219]	ASMS	0.51	1.85	0.21	115.09	2013
6	[217]	S3Tracker	0.52	1.77	0.24	14.27	2015
7	[161]	IVT	0.44	4.33	0.12	8.38	2008	子空间学习
8	[220]	CT	0.39	4.09	0.11	12.90	2012	子空间学习
9	[221]	L1APG	0.47	4.65	0.13	1.51	2012	稀疏表示
10	[222]	OAB	0.45	4.19	0.13	8.00	2014	Online Boosting
11	[223]	MCT	0.47	1.76	0.22	2.77	2011
12	[224]	CMIL	0.43	2.47	0.19	5.14	2010
13	[225]	Struck	0.47	1.61	0.25	2.44	2014	SVM
14	[217]	RobStruck	0.48	1.47	0.22	1.89	2015	SVM
15	[226]	MIL	0.42	3.11	0.17	5.99	2011	随机学习

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