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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参考文献(95)

[1]	Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422 doi: 10.1109/TPAMI.2011.239
[2]	孙红光. 基于小波分析的军事目标识别及跟踪方法研究[博士学位论文], 长春理工大学, 中国, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10186-2009201547.htm Sun Hong-Guang. The Study of Military Affairs Target Recognition and Tracking Method Based on Wavelet Analysis[Ph.D. dissertation], Changchun University of Science and Technology, China, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10186-2009201547.htm
[3]	Rautaray S S, Agrawal A. Vision based hand gesture recognition for human computer interaction:a survey. Artificial Intelligence Review, 2015, 43(1):1-54 doi: 10.1007/s10462-012-9356-9
[4]	Thakoor N S, An L, Bhanu B, Sunderrajan S, Manjunath B S. People tracking in camera networks:three open questions. Computer, 2015, 48(3):78-86 doi: 10.1109/MC.2015.83
[5]	Ess A, Schindler K, Leibe B, Van Gool L. Object detection and tracking for autonomous navigation in dynamic environments. The International Journal of Robotics Research, 2010, 29(14):1707-1725 doi: 10.1177/0278364910365417
[6]	Dinh T B, Vo N, Medioni G. Context tracker:exploring supporters and distracters in unconstrained environments. In:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Colorado Springs, CO, USA:IEEE, 2011. 1177-1184 http://dl.acm.org/citation.cfm?id=2191959
[7]	Choi W. Near-online multi-target tracking with aggregated local flow descriptor. In:Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile:IEEE, 2015. 3029-3037
[8]	Alismail H, Browning B, Lucey S. Robust tracking in low light and sudden illumination changes. In:Proceedings of the 4th International Conference on 3D Vision (3DV). Stanford, CA, USA:IEEE, 2016. 389-398 https://www.computer.org/csdl/proceedings/3dv/2016/5407/00/5407a389-abs.html
[9]	王江峰. 基于轨迹片段关联的目标跟踪与事件检测方法研究[博士学位论文], 国防科学技术大学, 中国, 2011. http://cdmd.cnki.com.cn/Article/CDMD-90002-1012020821.htm Wang Jiang-Feng. Researches on Object Tracking and Event Detection Based on Tracklet Association[Ph.D. dissertation], National University of Defense Technology, China, 2011. http://cdmd.cnki.com.cn/Article/CDMD-90002-1012020821.htm
[10]	Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786):504-507 doi: 10.1126/science.1127647
[11]	Le N, Heili A, Odobez J M. Long-term time-sensitive costs for CRF-based tracking by detection. In:European Conference on Computer Vision. Amsterdam, The Netherlands:Springer International Publishing, 2016. 43-51
[12]	Lan X S, Xiong Z W, Zhang W, Li S X, Chang H X, Zeng W J. A super-fast online face tracking system for video surveillance. In:Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS). Montreal, QC, Canada:IEEE, 2016. 1998-2001 http://ieeexplore.ieee.org/document/7538968/
[13]	Huang C H, Allain B, Franco J S, Navab N, Ilic S, Boyer E. Volumetric 3D tracking by detection. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA:IEEE, 2016. 3862-3870 https://www.computer.org/csdl/proceedings/cvpr/2016/8851/00/8851d862-abs.html
[14]	尹宏鹏, 陈波, 柴毅, 刘兆栋.基于视觉的目标检测与跟踪综述.自动化学报, 2016, 42(10):1466-1489 http://www.aas.net.cn/CN/abstract/abstract18935.shtml Yin Hong-Peng, Chen Bo, Chai Yi, Liu Zhao-Dong. Vision-based object detection and tracking:a review. Acta Automatica Sinica, 2016, 42(10):1466-1489 http://www.aas.net.cn/CN/abstract/abstract18935.shtml
[15]	Wang X Y, Han T X, Yan S C. An HOG-LBP human detector with partial occlusion handling. In:Proceedings of the 12th International Conference on Computer Vision (ICCV). Kyoto, Japan:IEEE, 2009. 32-39 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5459207
[16]	Cong Y, Liu W Y, Zhang Y L, Liang H. The research of video tracking based on improved SIFT algorithm. In:Proceedings of the 2016 IEEE International Conference on Mechatronics and Automation (ICMA). Harbin, China:IEEE, 2016. 1703-1707 http://ieeexplore.ieee.org/document/7558820/
[17]	Dewan M A A, Granger E, Marcialis G L, Sabourin R, Roli F. Adaptive appearance model tracking for still-to-video face recognition. Pattern Recognition, 2016, 49:129-151 doi: 10.1016/j.patcog.2015.08.002
[18]	黄凯奇, 陈晓棠, 康运锋, 谭铁牛.智能视频监控技术.计算机学报, 2015, 38(6):1093-1118 doi: 10.11897/SP.J.1016.2015.01093 Huang Kai-Qi, Chen Xiao-Tang, Kang Yun-Feng, Tan Tie-Niu. Intelligent visual surveillance:a review. Chinese Journal of Computers, 2015, 38(6):1093-1118 doi: 10.11897/SP.J.1016.2015.01093
[19]	王坤峰, 苟超, 段艳杰, 林懿伦, 郑心湖, 王飞跃.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml Wang Kun-Feng, Gou Chao, Duan Yan-Jie, Lin Yi-Lun, Zheng Xin-Hu, Wang Fei-Yue. Generative adversarial networks:the state of the art and beyond. Acta Automatica Sinica, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml
[20]	Hua K L, Sari I N, Yeh M C. Human pose tracking using online latent structured support vector machine. In:Proceedings of the 23rd International Conference on Multimedia Modeling. Reykjavik, Iceland:Springer, 2017. 626-637 https://www.researchgate.net/publication/311992762_Human_Pose_Tracking_Using_Online_Latent_Structured_Support_Vector_Machine
[21]	Xiang X Z, Bao W L, Tang H W, Li J J, Wei Y M. Vehicle detection and tracking for gas station surveillance based on AdaBoosting and optical flow. In:Proceedings of the 12th World Congress on Intelligent Control and Automation (WCICA). Guilin, China:IEEE, 2016. 818-821 http://ieeexplore.ieee.org/document/7578324/
[22]	缪源. 图像匹配算法的研究[硕士学位论文], 合肥工业大学, 中国, 2013. http://cdmd.cnki.com.cn/Article/CDMD-10359-1013377541.htm Miao Yuan. Research of Image Matching Algorithm[Master dissertation], Hefei University of Technology, China, 2013. http://cdmd.cnki.com.cn/Article/CDMD-10359-1013377541.htm
[23]	LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324 doi: 10.1109/5.726791
[24]	陆宗骐.图象处理领域轮廓跟踪及应用.中国计算机用户, 1994, (10):49-52 http://d.wanfangdata.com.cn/Thesis/Y619531
[25]	张继平, 刘直芳.背景估计与运动目标检测跟踪.计算技术与自动化, 2004, 23(4):51-54 http://d.wanfangdata.com.cn/Periodical/jsjsyzdh200404017 Zhang Ji-Ping, Liu Zhi-Fang. Background estimation and moving target detection. Computing Technology and Automation, 2004, 23(4):51-54 http://d.wanfangdata.com.cn/Periodical/jsjsyzdh200404017
[26]	Adams R, Bischof L. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(6):641-647 doi: 10.1109/34.295913
[27]	林开颜, 吴军辉, 徐立鸿.彩色图像分割方法综述.中国图象图形学报, 2005, 10(1):1-10 http://d.wanfangdata.com.cn/Periodical/zgtxtxxb-a200501001 Lin Kai-Yan, Wu Jun-Hui, Xu Li-Hong. A survey on color image segmentation techniques. Journal of Image and Graphics, 2005, 10(1):1-10 http://d.wanfangdata.com.cn/Periodical/zgtxtxxb-a200501001
[28]	韩思奇, 王蕾.图像分割的阈值法综述.系统工程与电子技术, 2002, 24(6):91-94 http://d.wanfangdata.com.cn/Periodical/xtgcydzjs200206027 Han Si-Qi, Wang Lei. A survey of thresholding methods for image segmentation. Systems Engineering and Electronics, 2002, 24(6):91-94 http://d.wanfangdata.com.cn/Periodical/xtgcydzjs200206027
[29]	王惠明, 史萍.图像纹理特征的提取方法.中国传媒大学学报自然科学版, 2006, 13(1):49-52 http://d.wanfangdata.com.cn/Periodical/bjgbxyxb200601009 Wang Hui-Ming, Shi Ping. Methods to extract images texture features. Journal of Communication University of China Science and Technology, 2006, 13(1):49-52 http://d.wanfangdata.com.cn/Periodical/bjgbxyxb200601009
[30]	汪启伟. 图像直方图特征及其应用研究[博士学位论文], 中国科学技术大学, 中国, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10358-1014189442.htm Wang Qi-Wei. Study on image histogram feature and application[Ph.D. dissertation], University of Science and Technology of China, China, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10358-1014189442.htm
[31]	丁明跃, 常金玲, 彭嘉雄.不变矩算法研究.数据采集与处理, 1992, 7(1):1-9 http://d.wanfangdata.com.cn/Periodical/dzkxxk200807041 Ding Ming-Yue, Chang Jin-Ling, Peng Jia-Xiong. Research on moment invariants algorithm. Journal of Data Acquisition & Processing, 1992, 7(1):1-9 http://d.wanfangdata.com.cn/Periodical/dzkxxk200807041
[32]	严柏军, 郑链, 王克勇.基于不变矩特征匹配的快速目标检测算法.红外技术, 2001, 23(6):8-12 http://d.wanfangdata.com.cn/Periodical/hwjs200106003 Yan Bo-Jun, Zheng Lian, Wang Ke-Yong. Fast target-detecting algorithm based on invariant moment. Infrared Technology, 2001, 23(6):8-12 http://d.wanfangdata.com.cn/Periodical/hwjs200106003
[33]	张伟, 何金国. Hu不变矩的构造与推广.计算机应用, 2010, 30(9):2449-2452 http://d.wanfangdata.com.cn/Periodical/jsjyy201009046 Zhang Wei, He Jin-Guo. Construction and generalization of Hu moment invariants. Journal of Computer Application, 2010, 30(9):2449-2452 http://d.wanfangdata.com.cn/Periodical/jsjyy201009046
[34]	刘进, 张天序.图像不变矩的推广.计算机学报, 2004, 27(5):668-674 http://d.wanfangdata.com.cn/Periodical/jsjxb200405012 Liu Jin, Zhang Tian-Xu. The generalization of moment invariants. Chinese Journal of Computers, 2004, 27(5):668-674 http://d.wanfangdata.com.cn/Periodical/jsjxb200405012
[35]	洪子泉, 杨静宇.用于图象识别的图象代数特征抽取.自动化学报, 1992, 18(2):233-238 http://www.aas.net.cn/CN/abstract/abstract14490.shtml Hong Zi-Quan, Yang Jing-Yu. Algebraic feature extraction of images for recognition. Acta Automatica Sinica, 1992, 18(2):233-238 http://www.aas.net.cn/CN/abstract/abstract14490.shtml
[36]	赵峰, 黄庆明, 高文.一种基于奇异值分解的图像匹配算法.计算机研究与发展, 2010, 47(1):23-32 http://d.wanfangdata.com.cn/Periodical/jsjyjyfz201001004 Zhao Feng, Huang Qing-Ming, Gao Wen. An image matching algorithm based on singular value decomposition. Journal of Computer Research and Development, 2010, 47(1):23-32 http://d.wanfangdata.com.cn/Periodical/jsjyjyfz201001004
[37]	蒋明, 张桂林, 胡若澜, 陈朝阳.基于主成分分析的图像匹配方法研究.红外与激光工程, 2000, 29(4):17-21 http://d.wanfangdata.com.cn/Periodical/hwyjggc200004006 Jiang Ming, Zhang Gui-Lin, Hu Ruo-Lan, Chen Zhao-Yang. Research of an image matching method based on principal component analysis. Infrared and Laser Engineering, 2000, 29(4):17-21 http://d.wanfangdata.com.cn/Periodical/hwyjggc200004006
[38]	杨竹青, 李勇, 胡德文.独立成分分析方法综述.自动化学报, 2002, 28(5):762-772 http://www.aas.net.cn/CN/abstract/abstract16161.shtml Yang Zhu-Qing, Li Yong, Hu De-Wen. Independent component analysis:a survey. Acta Automatica Sinica, 2002, 28(5):762-772 http://www.aas.net.cn/CN/abstract/abstract16161.shtml
[39]	张春美, 龚志辉, 孙雷.改进SIFT特征在图像匹配中的应用.计算机工程与应用, 2008, 44(2):95-97 http://d.wanfangdata.com.cn/Periodical/jsjgcyyy200802029 Zhang Chun-Mei, Gong Zhi-Hui, Sun Lei. Improved SIFT feature applied in image matching. Computer Engineering and Applications, 2008, 44(2):95-97 http://d.wanfangdata.com.cn/Periodical/jsjgcyyy200802029
[40]	Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA:IEEE, 2016. 4293-4302 http://ieeexplore.ieee.org/document/7780834/
[41]	Chen Y, Yang X N, Zhong B N, Pan S N, Chen D S, Zhang H Z. CNNTracker:online discriminative object tracking via deep convolutional neural network. Applied Soft Computing, 2016, 38:1088-1098 doi: 10.1016/j.asoc.2015.06.048
[42]	Bertinetto L, Valmadre J, Henriques J F, Vedaldi A, Torr P H S. Fully-convolutional siamese networks for object tracking. In:European Conference on Computer Vision. Amsterdam, The Netherlands:Springer, 2016. 850-865 doi: 10.1007/978-3-319-48881-3_56
[43]	赵亮, 刘建辉, 王星.基于Hellinger距离的混合数据集中分类变量相似度分析.计算机科学, 2016, 43(6):280-282 doi: 10.11896/j.issn.1002-137X.2016.06.055 Zhao Liang, Liu Jian-Hui, Wang Xing. Hellinger distance based similarity analysis for categorical variables in mixture dataset. Computer Science, 2016, 43(6):280-282 doi: 10.11896/j.issn.1002-137X.2016.06.055
[44]	宣国荣, 柴佩琪.基于巴氏距离的特征选择.模式识别与人工智能, 1996, 9(4):324-329 http://d.wanfangdata.com.cn/Periodical/jsjgcyyy200436028 Xuan Guo-Rong, Chai Pei-Qi. Feature selection based on Bhattacharyya distance. PR & AI, 1996, 9(4):324-329 http://d.wanfangdata.com.cn/Periodical/jsjgcyyy200436028
[45]	何天晓, 常玉堂.多元插值法.工科数学, 1985, (1):12-16 http://d.wanfangdata.com.cn/Periodical/jxsjyzz201105031
[46]	Lo S C B, Chan H P, Lin J S, Li H, Freedman M T, Mun S K. Artificial convolution neural network for medical image pattern recognition. Neural Networks, 1995, 8(7-8):1201-1214 doi: 10.1016/0893-6080(95)00061-5
[47]	Ferryman J, Shahrokni A. PETS2009:dataset and challenge. In:Proceedings of the 20th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter). Snowbird, UT, USA:IEEE, 2009. 1-6 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5399556
[48]	Leal-Taixé L, Milan A, Reid I, Schindler K. MOTChallenge 2015:towards a benchmark for multi-target tracking. arXiv preprint arXiv:1504.01942, 2015. http://arxiv.org/abs/1504.01942
[49]	Fisher R B. The PETS04 surveillance ground-truth data sets. In:Proceedings of the 6th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. New York, USA:IEEE, 2004. 1-5 http://www.researchgate.net/publication/228745046_the_pets04_surveillance_ground-truth_data_sets
[50]	Home Office Scientific Development Branch. Imagery library for intelligent detection systems (i-LIDS). In:Proceedings of the 2006 Institution of Engineering and Technology Conference on Crime and Security. London, UK:IET, 2006. 445-448 http://ieeexplore.ieee.org/document/4123801/
[51]	Wen L Y, Du D W, Cai Z W, Lei Z, Chang M C, Qi H G, Lim J, Yang M H, Lyu S. UA-DETRAC:a new benchmark and protocol for multi-object detection and tracking. arXiv preprint arXiv:1511.04136, 2015. http://arxiv.org/abs/1511.04136
[52]	Alahi A, Ramanathan V, Li F F. Socially-aware large-scale crowd forecasting. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA:IEEE, 2014. 2203-2210 http://ieeexplore.ieee.org/document/6909680/
[53]	Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? the KITTI vision benchmark suite. In:Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI, USA:IEEE, 2012. 3354-3361 https://www.computer.org/csdl/proceedings/cvpr/2012/1226/00/424O3C04-abs.html
[54]	Ros G, Sellart L, Materzynska J, Vazquez D, Lopez A M. The SYNTHIA dataset:a large collection of synthetic images for semantic segmentation of urban scenes. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA:IEEE, 2016. 3234-3243 http://ieeexplore.ieee.org/document/7780721/
[55]	Huang C, Wu B, Nevatia R. Robust object tracking by hierarchical association of detection responses. In:Proceedings of the 10th European Conference on Computer Vision. Marseille, France:Springer, 2008. 788-801 http://www.springerlink.com/content/d426ur512533w32n
[56]	Richard M D, Lippmann R P. Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation, 1991, 3(4):461-483 doi: 10.1162/neco.1991.3.4.461
[57]	Greig D M, Porteous B T, Seheult A H. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society. Series B (Methodological), 1989, 51(2):271-279 http://www.citeulike.org/user/mstone/article/2067236
[58]	Yang B, Nevatia R. Multi-target tracking by online learning a CRF model of appearance and motion patterns. International Journal of Computer Vision, 2014, 107(2):203-217 doi: 10.1007/s11263-013-0666-4
[59]	Overett G, Petersson L, Brewer N, Andersson L, Pettersson N. A new pedestrian dataset for supervised learning. In:Proceedings of the 2008 IEEE Intelligent Vehicles Symposium. Eindhoven, Netherlands:IEEE, 2008. 373-378 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4621297
[60]	Wu B Y, Lyu S, Hu B G, Ji Q. Simultaneous clustering and tracklet linking for multi-face tracking in videos. In:Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV). Sydney, NSW, Australia:IEEE, 2013. 2856-2863 http://dl.acm.org/citation.cfm?id=2587103
[61]	Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2):137-154 doi: 10.1023/B:VISI.0000013087.49260.fb
[62]	Leung V, Herbin S. Flexible tracklet association for complex scenarios using a Markov Logic Network. In:Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). Barcelona, Spain:IEEE, 2011. 1870-1875 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6130476
[63]	Wang B, Wang G, Luk Chan K, Wang L. Tracklet association with online target-specific metric learning. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA:IEEE, 2014. 1234-1241 https://www.computer.org/csdl/proceedings/cvpr/2014/5118/00/5118b234-abs.html
[64]	Wu Z, Kunz T H, Betke M. Efficient track linking methods for track graphs using network-flow and set-cover techniques. In:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Colorado Springs, CO, USA:IEEE, 2011. 1185-1192 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5995515
[65]	Shitrit H B, Berclaz J, Fleuret F, Fua P. Multi-commodity network flow for tracking multiple people. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8):1614-1627 doi: 10.1109/TPAMI.2013.210
[66]	Song B, Jeng T Y, Staudt E, Roy-Chowdhury A K. A stochastic graph evolution framework for robust multi-target tracking. In:Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece:Springer, 2010. 605-619 http://dl.acm.org/citation.cfm?id=1886109
[67]	Geyer C J. Practical Markov chain monte Carlo. Statistical Science, 1992, 7(4):473-483 doi: 10.1214/ss/1177011137
[68]	Zamir A R, Dehghan A, Shah M. GMCP-tracker:global multi-object tracking using generalized minimum clique graphs. Computer Vision——ECCV 2012. Berlin, Heidelberg:Springer, 2012. 343-356 doi: 10.1007/978-3-642-33709-3_25
[69]	Wang B, Wang L, Shuai B, Zuo Z, Liu T, Chan K L, Wang G. Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Las Vegas, NV, USA:IEEE, 2016. 1-8 http://ieeexplore.ieee.org/document/7789545/
[70]	Gold S, Rangarajan A. Softmax to softassign:neural network algorithms for combinatorial optimization. Journal of Artificial Neural Networks, 1996, 2(4):381-399 http://www.academia.edu/25129547/Softmax_to_Softassign_Neural_Network_Algorithms_for_Combinatorial_Optimization
[71]	Qin Z, Shelton C R. Improving multi-target tracking via social grouping. In:Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI, USA:IEEE, 2012. 1972-1978 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6247899
[72]	Sun X, Zhu S H, Jin D L, Liang Z W, Xu G Z. Tracklet association for object tracking. In:Proceedings of the 2016 Chinese Control and Decision Conference (CCDC). Yinchuan, China:IEEE, 2016. 107-112 http://ieeexplore.ieee.org/document/7530963/
[73]	Xing J L, Ai H Z, Lao S H. Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In:Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Miami, FL, USA:IEEE, 2009. 1200-1207 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5206745
[74]	Bae S H, Yoon K J. Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In:Proceedings of the 2014 IEEE conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA:IEEE, 2014. 1218-1225 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6909555
[75]	Yang B, Nevatia R. Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. In:Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI, USA:IEEE, 2012. 1918-1925 http://dl.acm.org/citation.cfm?id=2354940
[76]	Kuo C H, Nevatia R. How does person identity recognition help multi-person tracking? In:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Colorado Springs, CO, USA:IEEE, 2011. 1217-1224 http://dl.acm.org/citation.cfm?id=2191740.2191963
[77]	Kumar G, Bhatia P K. A detailed review of feature extraction in image processing systems. In:Proceedings of the 4th International Conference on Advanced Computing & Communication Technologies (ACCT). Rohtak, India:IEEE, 2014. 5-12 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6783417
[78]	Kulchandani J S, Dangarwala K J. Moving object detection:review of recent research trends. In:Proceedings of the 2015 International Conference on Pervasive Computing (ICPC). Pune, India:IEEE, 2015. 1-5 http://ieeexplore.ieee.org/document/7087138/
[79]	Shukla A P, Saini M. "Moving object tracking of vehicle detection":a concise review. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, 8(3):169-176 doi: 10.14257/ijsip
[80]	丁忠校.视频监控图像的运动目标检测方法综述.电视技术, 2008, 32(5):72-76 http://d.wanfangdata.com.cn/Periodical/dsjs200805027 Ding Zhong-Xiao. Survey on moving object detection methods for video surveillance images. Video Engineering, 2008, 32(5):72-76 http://d.wanfangdata.com.cn/Periodical/dsjs200805027
[81]	Moussaïd M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(17):6884-6888 doi: 10.1073/pnas.1016507108
[82]	Helbing D, Farkas I, Vicsek T. Simulating dynamical features of escape panic. Nature, 2000, 407(6803):487-490 doi: 10.1038/35035023
[83]	Courty N, Allain P, Creusot C, Corpetti T. Using the AGORASET dataset:assessing for the quality of crowd video analysis methods. Pattern Recognition Letters, 2014, 44:161-170 doi: 10.1016/j.patrec.2014.01.004
[84]	王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策, 2004, 19(5):485-489, 514 http://d.wanfangdata.com.cn/Periodical/kzyjc200405002 Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5):485-489, 514 http://d.wanfangdata.com.cn/Periodical/kzyjc200405002
[85]	王飞跃.平行控制:数据驱动的计算控制方法.自动化学报, 2013, 39(4):293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml Wang Fei-Yue. Parallel control:a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4):293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml
[86]	白天翔, 王帅, 沈震, 曹东璞, 郑南宁, 王飞跃.平行机器人与平行无人系统:框架、结构、过程、平台及其应用.自动化学报, 2017, 43(2):161-175 http://www.aas.net.cn/CN/abstract/abstract18998.shtml Bai Tian-Xiang, Wang Shuai, Shen Zhen, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel robotics and parallel unmanned systems:framework, structures, process, platform and applications. Acta Automatica Sinica, 2013, 43(2), 161-175 http://www.aas.net.cn/CN/abstract/abstract18998.shtml
[87]	白天翔, 王帅, 赵学亮, 秦继荣.平行武器:迈向智能战争的武器.指挥与控制学报, 2017, 3(2):89-98 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=zhkz201702001&dbname=CJFD&dbcode=CJFQ Bai Tian-Xiang, Wang Shuai, Zhao Xue-Liang, Qin Ji-Rong. Parallel weapons:weapons towards intelligent warfare. Journal of Command and Control, 2017, 3(2):89-98 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=zhkz201702001&dbname=CJFD&dbcode=CJFQ
[88]	李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃.平行学习——机器学习的一个新型理论框架.自动化学报, 2017, 43(1):1-8 http://www.aas.net.cn/CN/abstract/abstract18984.shtml Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning——a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1):1-8 http://www.aas.net.cn/CN/abstract/abstract18984.shtml
[89]	Li L, Lin Y L, Zheng N N, Wang F Y. Parallel learning:a perspective and a framework. IEEE/CAA Journal of Automatica Sinica, 2017, 4(3):389-395 doi: 10.1109/JAS.2017.7510493
[90]	刘昕, 王晓, 张卫山, 汪建基, 王飞跃.平行数据:从大数据到数据智能.模式识别与人工智能, 2017, 30(8):673-681 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=mssb201708001&dbname=CJFD&dbcode=CJFQ Liu Xin, Wang Xiao, Zhang Wei-Shan, Wang Jian-Ji, Wang Fei-Yue. Parallel data:from big data to data intelligence. Pattern Recognition and Artificial Intelligence, 2017, 30(8):673-681 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=mssb201708001&dbname=CJFD&dbcode=CJFQ
[91]	Wang F Y. Scanning the issue and beyond:parallel driving with software vehicular robots for safety and smartness. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4):1381-1387 doi: 10.1109/TITS.2014.2342451
[92]	Wang F Y, Zheng N N, Cao D P, Martinez C M, Li L, Liu T. Parallel driving in CPSS:a unified approach for transport automation and vehicle intelligence. IEEE/CAA Journal of Automatica Sinica, 2017, 4(4):577-587 doi: 10.1109/JAS.2017.7510598
[93]	王坤峰, 苟超, 王飞跃.平行视觉:基于ACP的智能视觉计算方法.自动化学报, 2016, 42(10):1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml Wang Kun-Feng, Gou Chao, Wang Fei-Yue. Parallel vision:an ACP-based approach to intelligent vision computing. Acta Automatica Sinica, 2016, 42(10):1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml
[94]	Wang K F, Gou C, Zheng N N, Rehg J M, Wang F Y. Parallel vision for perception and understanding of complex scenes:methods, framework, and perspectives. Artificial Intelligence Review, 2017, 48(3):299-329 doi: 10.1007/s10462-017-9569-z
[95]	王坤峰, 鲁越, 王雨桐, 熊子威, 王飞跃.平行图像:图像生成的一个新型理论框架.模式识别与人工智能, 2017, 30(7):577-587 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=mssb201707001&dbname=CJFD&dbcode=CJFQ Wang Kun-Feng, Lu Yue, Wang Yu-Tong, Xiong Zi-Wei, Wang Fei-Yue. Parallel imaging:a new theoretical framework for image generation. Pattern Recognition and Artificial Intelligence, 2017, 30(7):577-587 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=mssb201707001&dbname=CJFD&dbcode=CJFQ