基于踪片Tracklet关联的视觉目标跟踪：现状与展望

刘雅婷; 王坤峰; 王飞跃

doi:10.16383/j.aas.2017.c170117

基于踪片Tracklet关联的视觉目标跟踪：现状与展望

doi: 10.16383/j.aas.2017.c170117

刘雅婷^1,2,3,,
王坤峰^1,3,,
王飞跃^1,4, ,

1.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190
2.
中国科学院大学北京 100049
3.
青岛智能产业技术研究院青岛 266000
4.
国防科技大学军事计算实验与平行系统技术研究中心长沙 410073

基金项目:

国家自然科学基金 91520301

国家自然科学基金 61533019

国家自然科学基金 71232006

详细信息

作者简介:
刘雅婷中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为视觉目标跟踪, 机器学习.E-mail:liuyating2015@ia.ac.cn

王坤峰中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员.主要研究方向为智能交通系统, 智能视觉计算, 机器学习.E-mail:kunfeng.wang@ia.ac.cn

通讯作者:
王飞跃中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科技大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn

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出版历程
- 收稿日期: 2017-03-04
- 录用日期: 2017-08-18
- 刊出日期: 2017-11-20

Tracklet Association-based Visual Object Tracking:The State of the Art and Beyond

LIU Ya-Ting^{1,2,3
,},
WANG Kun-Feng^{1,3
,},
WANG Fei-Yue^{1,4
, ,}

1.
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Qingdao Academy of Intelligent Industries, Qingdao 266000
4.
Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073

Funds:

National Natural Science Foundation of China 91520301

National Natural Science Foundation of China 61533019

National Natural Science Foundation of China 71232006

More Information

Author Bio:
Ph. D. candidate at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. Her research interest covers visual object tracking and machine learning

Associate professor at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent transportation systems, intelligent vision computing, and machine learning

Corresponding author: WANG Fei-Yue Professor at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of the Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology. His research interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper

摘要

摘要: 近年来，由于计算机视觉技术的发展和计算机硬件性能的提高，基于视觉的目标跟踪方法得到了飞速的发展.其中，基于踪片（Tracklet）关联的目标跟踪方法因为具有对目标遮挡的强鲁棒性、算法运行的快速性等优点得到了广泛关注，本文对这类方法的最新研究进展进行了综述.首先，简明地介绍了视觉目标跟踪的基本知识、研究意义和研究现状.然后，通过感兴趣目标检测、跟踪特征提取、踪片生成、踪片关联与补全四个步骤，系统详尽地介绍了基于踪片关联的目标跟踪方法，分析了近年来提出的一些踪片关联方法的优缺点.最后，本文指出了该研究问题的发展方向，一方面要提出更先进的目标跟踪模型，另一方面要采用平行视觉方法进行虚实互动的模型学习与评估.
- 视觉目标跟踪 /
- 踪片关联 /
- 网络流 /
- 马尔科夫随机场 /
- 平行视觉
Abstract: In the past decade, benefitting from the progress in computer vision theories and computing resources, there has been a rapid development in visual object tracking. Among all the methods, the tracklet-based object tracking method has gained its popularity due to its robustness in occlusion scenarios and high computational efficiency. This paper present a comprehensive survey of research methods related to tracklet-based object tracking. First, the basic concepts, research significance and research status of visual object tracking are introduced briefly. Then, the tracklet-based tracking approach is described from four aspects, including object detection, feature extraction, tracklet generation, and tracklet association and completion. Afterwards, we propose a detailed review and analyze the characteristics of state-of-the-art tracklet-based tracking methods. Finally, potential challenges and research fields are discussed. In our opinion, more advanced object tracking models should be proposed and the parallel vision approach should be adopted to learn and evaluate tracking models in a virtual-real interactive way.
- Visual object tracking /
- tracklet association /
- network flow /
- Markov random field /
- parallel vision
注释:

1) 本文责任编委张军平

HTML全文

图 1 基于踪片关联的视觉目标跟踪方法流程图

Fig. 1 Flowchart of visual object tracking based on tracklet association

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图 2 位置相关性示意图

Fig. 2 Sketch map of position relations

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图 3 踪片对运动相似性估计^[58]

Fig. 3 Estimation of motion similarity between a pair of tracklets^[58]

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图 4 有遮挡的目标跟踪轨迹^[62]

Fig. 4 Tracklet association for occluded objects^[62]

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图 5 特定目标度量的踪片关联框架图^[63]

Fig. 5 Framework of tracklet association through target-specific metric learning^[63]

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图 6 二分图算法和GMCP算法比较^[68]

Fig. 6 The comparison of bipartite and GMCP matching^[68]

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图 7 基于卷积神经网络和时空约束的踪片关联示意图[69

Fig. 7 Illustration of tracklet association based on convolutional neural networks and spatio-temporal constraint^[69]

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图 8 基本关联与社交组关联结合^[71]

Fig. 8 Illustration of the combination of tracklet association and social grouping^[71]

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图 9 不同场景下的人群动力学模型^[83]

Fig. 9 The crowd dynamic models in different scenes^[83]

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图 10 平行视觉的基本框架与体系结构^[93]

Fig. 10 Basic framework and architecture for parallel vision^[93]

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表 1 多目标跟踪常见的公共数据集

Table 1 Frequently used public datasets for multi-target tracking research

数据集	建立时间	描述	规模	类型
PETS^[47]	2009年	拥挤的公共区域多传感器跟踪和事件识别	3个不同环境视频序列8个视角	实际数据集
MOT challenge^[48]	2015年	不仅标记了行人, 车辆、静态的人、遮挡物体等都被标注	22个视频序列, 共11 286帧图像	实际数据集
CAVIAR^[49]	2003年	行人会面、购物, 穿越拥挤人群及在公共场所遗失行李等复杂场景	28段视频	实际数据集
i-LIDS^[50]	2006年	多摄像机配置, 可以选择多视角的数据进行实验	10小时视频	实际数据集
UA-DETRAC^[51]	2015年	多个数据采集地; 涉及汽车、公共汽车、货车等多种车辆; 包含多云、夜晚、晴天和下雨等天气条件	10小时视频	实际数据集
文献[52]中的数据集	2014年	从拥挤繁忙的火车站采集	42 million的轨迹	实际数据集
KITTI^[53]	2012年	每幅图像多达15辆车和30个行人; 包含三维立体, 光流, 视觉光度法, 3D物体检测和3D跟踪	50个视频序列	实际数据集
Virtual KITTI^[46]	2016年	数据从不同的成像和天气条件下的五个虚拟世界生成.有准确, 完整的2D和3D多对象跟踪注释, 并有像素级别和实例级别标签, 以及深度标签	50个高分辨率单目视频, 共21 260帧	虚拟数据集
SYNTHIA^[54]	2016年	多样化的场景; 多种动态物体种类; 多季节; 不同的照明条件和天气情况; 多传感器多视角	2分23秒雪景及1分48秒傍晚车载视频序列	虚拟数据集

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表 2 踪片关联跟踪方法在公共数据集上的测试情况表

Table 2 Testing results of tracklet association-based tracking methods on public datasets

方法	数据集	MT (%)	ML (%)	ML (%)	评价指标 FRAG IDS MOTA MOTP
文献[58]	PETS09	89.9	0.0	13	0
文献[58]	TUD	70.0	0.0	1	0
文献[63]	TUD	100	0.0	3	0
文献[66]	CAVIAR	84.0	4.0	6	8
文献[68]	PETS09-view1				8	90.3 %	69.02 %
文献[69]	PETS09	94.7	0.0	8	4	95.8 %	86.4 %
文献[69]	MOT Challenge	11.2	44.8	943	712	29.6 %	71.8 %
文献[71]	CAVIAR	88.0	2.6	5	6
文献[73]	CAVIAR	84.3	3.6		14
文献[75]	PETS09	89.5	0.0	21	15
文献[75]	CAVIAR	89.1	0.7	11	5

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