计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 40-49.doi: 10.11896/jsjkx.201100186

所属专题: 多媒体技术进展

• 多媒体技术进展* 上一篇    下一篇

视觉目标跟踪十年研究进展

张开华, 樊佳庆, 刘青山   

  1. 南京信息工程大学江苏省大数据分析技术重点实验室 南京210044
  • 收稿日期:2020-11-26 修回日期:2021-01-02 出版日期:2021-03-15 发布日期:2021-03-05
  • 通讯作者: 张开华(zhkhua@gmail.com)
  • 基金资助:
    :国家新一代人工智能重大项目(2018AAA0100400);国家自然科学基金(61872189);江苏省333工程人才项目(BRA2020291)

Advances on Visual Object Tracking in Past Decade

ZHANG Kai-hua, FAN Jia-qing, LIU Qing-shan   

  1. Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China
  • Received:2020-11-26 Revised:2021-01-02 Online:2021-03-15 Published:2021-03-05
  • About author:ZHANG Kai-hua,born in 1983,Ph.D,professor.His main research interests include image segmentation,level sets and visual tracking.
  • Supported by:
    National Major Project of China for New Generation of AI(2018AAA0100400),National Natural Science Foundation of China(61872189) and 333 High-level Talents Cultivation Project of Jiangsu Province(BRA2020291).

摘要: 视觉目标跟踪指在一个视频序列中,给定第一帧目标区域,在后续帧中自动匹配到该目标区域的任务。通常来说,由于场景遮挡、光照变化、物体本身形变等复杂因素,目标与场景的表观会发生剧烈的变化,这使得跟踪任务本身面临极大的挑战。在过去的十年中,随着深度学习在计算机视觉领域的广泛应用,目标跟踪领域也迅速发展,研究人员提出了一系列优秀算法。鉴于该领域处于快速发展的阶段,文中对视觉目标跟踪研究进行了综述,内容主要包括跟踪的基本框架改进、目标表示改进、空间上下文改进、时序上下文改进、数据集和评价指标改进等;另外,还综合分析了这些改进方法各自的优缺点,并提出了可能的未来的研究趋势。

关键词: 计算机视觉, 深度学习, 视觉目标跟踪

Abstract: Visual object tracking is a task in which the target region of the first frame in a video sequence is given,and then the target area is automatically matched in subsequent frames.Generally speaking,due to the complex factors such as scene occlusion,illumination change and object deformation,the appearance of the target and scene will change dramatically,which makes the tracking task itself is extremely challenging.In the past decade,with the extensive application of deep learning in the field of computer vision,the field of target tracking has also developed rapidly,resulting in a series of excellent algorithms.In view of this rapid development stage,this paper aims to provide a comprehensive review of visual object tracking research,mainly including the following aspects:the improvement of the basic framework of tracking,the improvement of target representation,the improvement of spatial context,the improvement of temporal context,the improvement of data sets and evaluation indicators.This paper also analyzes the advantages and disadvantages of these methods,and puts forward the possible future research trends.

Key words: Computer vision, Deep learning, Visual object tracking

中图分类号: 

  • TP391
[1]LI X,HU W,SHEN C,et al.A survey of appearance models in visual object tracking[J].ACM transactions on Intelligent Systems and Technology (TIST),2013,4(4):1-48.
[2]SMEULDERS A W M,CHU D M,CUCCHIARA R,et al.Visual tracking:An experimental survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,36(7):1442-1468.
[3]COMANICIU D,RAMESH V,MEER P.Kernel-based objecttracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5):564-577.
[4]AVIDAN S.Ensemble tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(2):261-271.
[5]ROSS D A,LIM J,LIN R S,et al.Incremental learning for robust visual tracking[J].International Journal of Computer Vision,2008,77(1-3):125-141.
[6]BABENKO B,YANG M H,BELONGIE S.Visual tracking with online multiple instance learning[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:983-990.
[7]MEI X,LING H.Robust visual tracking using ℓ1 minimization[C]//2009 IEEE 12th International Conference on Computer Vision.IEEE,2009:1436-1443.
[8]KALAL Z,MATAS J,MIKOLAJCZYK K.Pn learning:Bootstrapping binary classifiers by structural constraints[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:49-56.
[9]BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:2544-2550.
[10]HARE S,GOLODETZ S,SAFFARI A,et al.Struck:Structured output tracking with kernels[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(10):2096-2109.
[11]JIA X,LU H,YANG M H.Visual tracking via adaptive structural local sparse appearance model[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:1822-1829.
[12]ZHANG K,ZHANG L,YANG M H.Fast compressive tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(10):2002-2015.
[13]HENRIQUES J F,CASEIRO R,MARTINS P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,37(3):583-596.
[14]DANELLJAN M,SHAHBAZ K F,FELSBERG M,et al.Adaptive color attributes for real-time visual tracking[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:1090-1097.
[15]ZHANG K,ZHANG L,LIU Q,et al.Fast visual tracking via dense spatio-temporal context learning[C]//European Confe-rence on Computer Vision.Springer,Cham,2014:127-141.
[16]MA C,HUANG J B,YANG X,et al.Hierarchical convolutional features for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:3074-3082.
[17]DANELLJAN M,BHAT G,SHAHBAZ K F,et al.Eco:Effi-cient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:6638-6646.
[18]BERTINETTO L,VALMADRE J,HENRIQUES J F,et al.Fully-convolutional siamese networks for object tracking[C]//European Conference on Computer Vision.Springer,Cham,2016:850-865.
[19]LI B,YAN J,WU W,et al.High performance visual tracking with siamese region proposal network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:8971-8980.
[20]DANELLJAN M,BHAT G,KHAN F S,et al.Atom:Accurate tracking by overlap maximization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:4660-4669.
[21]VOIGTLAENDER P,LUITEN J,TORR P H S,et al.Siamr-cnn:Visual tracking by re-detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:6578-6588.
[22]YILMAZ A,JAVED O,SHAH M.Object tracking:A survey[J].Acm computing surveys (CSUR),2006,38(4):13.
[23]HU W,ZHOU X,LI W,et al.Active contour-based visualtracking by integrating colors,shapes,and motions[J].IEEE Transactions on Image Processing,2012,22(5):1778-1792.
[24]WU Y,LIM J,YANG M H.Online object tracking:A benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:2411-2418.
[25]KRISTAN M,MATAS J,LEONARDIS A,et al.The visual object tracking vot2015 challenge results[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2015:1-23.
[26]LIANG P,BLASCH E,LING H.Encoding color information for visual tracking:Algorithms and benchmark[J].IEEE Transactions on Image Processing,2015,24(12):5630-5644.
[27]MULLER M,BIBI A,GIANCOLA S,et al.Trackingnet:Alarge-scale dataset and benchmark for object tracking in the wild[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:300-317.
[28]KIANI G H,FAGG A,HUANG C,et al.Need for speed:Abenchmark for higher frame rate object tracking[C]//Procee-dings of the IEEE International Conference on Computer Vision.2017:1125-1134.
[29]FAN H,LIN L,YANG F,et al.Lasot:A high-quality benchmark for large-scale single object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:5374-5383.
[30]VALMADRE J,BERTINETTO L,HENRIQUES J F,et al.Long-term tracking in the wild:A benchmark[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:670-685.
[31]KRISTAN M,LEONARDIS A,MATAS J,et al.The visual object tracking vot2017 challenge results[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2017:1949-1972.
[32]KRISTAN M,LEONARDIS A,MATAS J,et al.The sixth vi-sual object tracking vot2018 challenge results[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018.
[33]KRISTAN M,MATAS J,LEONARDIS A,et al.The seventhvisual object tracking vot2019 challenge results[C]//Procee-dings of the IEEE International Conference on Computer Vision Workshops.2019:2206-2241.
[34]KRISTAN M,LUKEZIC A,DANELLJAN M,et al.The new VOT2020 short-term tracking performance evaluation protocol and measures[J/OL].https://data.votchallenge.net/vot2020/vot-2020-protocol.pdf.
[35]BERTINETTO L,VALMADRE J,GOLODETZ S,et al.Staple:Complementary learners for real-time tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1401-1409.
[36]BOLME D S,DRAPER B A,BEVERIDGE J R.Average of synthetic exact filters[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:2105-2112.
[37]ZHANG L,GONZALEZ-GARCIA A,WEIJER J,et al.Lear-ning the model update for siamese trackers[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:4010-4019.
[38]ZHU Z,WANG Q,LI B,et al.Distractor-aware siamese net-works for visual object tracking[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:101-117.
[39]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[J].Communications of the ACM,2017,60(6):84-90.
[40]GOODFELLOW I,BENGIO Y,COURVILLE A,et al.Deeplearning[M].Cambridge:MIT press,2016.
[41]RUSSAKOVSKY O,DENG J,SU H,et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[42]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
[43]WITTEN I H,FRANK E.Data mining:practical machine learning tools and techniques with Java implementations[J].Acm Sigmod Record,2002,31(1):76-77.
[44]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[45]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680.
[46]HE K,FAN H,WU Y,et al.Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:9729-9738.
[47]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587.
[48]GIRSHICK R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
[49]REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems.2015:91-99.
[50]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[51]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//European Conference on Computer Vision.Springer,Cham,2016:21-37.
[52]HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4700-4708.
[53]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440.
[54]RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention.Springer,Cham,2015:234-241.
[55]HE K,GKIOXARI G,DOLLÁR P,et al.Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2961-2969.
[56]HE K,GIRSHICK R,DOLLÁR P.Rethinking imagenet pre-training[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:4918-4927.
[57]MA C,YANG X,ZHANG C,et al.Long-term correlation tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:5388-5396.
[58]WANG L,OUYANG W,WANG X,et al.Visual tracking with fully convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:3119-3127.
[59]ZHANG K,LIU Q,WU Y,et al.Robust visual tracking via convolutional networks without training[J].IEEE Transactions on Image Processing,2016,25(4):1779-1792.
[60]QI Y,ZHANG S,QIN L,et al.Hedged deep tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:4303-4311.
[61]WANG L,OUYANG W,WANG X,et al.Stct:Sequentiallytraining convolutional networks for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1373-1381.
[62]HELD D,THRUN S,SAVARESE S.Learning to track at 100 fps with deep regression networks[C]//European Conference on Computer Vision.Springer,Cham,2016:749-765.
[63]ZHANG T,XU C,YANG M H.Multi-task correlation particle filter for robust object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4335-4343.
[64]GAO J,ZHANG T,YANG X,et al.Deep relative tracking[J].IEEE Transactions on Image Processing,2017,26(4):1845-1858.
[65]GUO Q,FENG W,ZHOU C,et al.Learning dynamic siamese network for visual object tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1763-1771.
[66]FAN H,LING H.Parallel tracking and verifying:A framework for real-time and high accuracy visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:5486-5494.
[67]SONG Y,MA C,GONG L,et al.Crest:Convolutional residual learning for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2555-2564.
[68]SONG Y,MA C,WU X,et al.Vital:Visual tracking via adversarial learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:8990-8999.
[69]ZHU Z,WU W,ZOU W,et al.End-to-end flow correlationtracking with spatial-temporal attention[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:548-557.
[70]ZHU Z,WANG Q,LI B,et al.Distractor-aware siamese net-works for visual object tracking[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:101-117.
[71]LU X,MA C,NI B,et al.Deep regression tracking with shrinkage loss[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:353-369.
[72]DONG X,SHEN J.Triplet loss in siamese network for object tracking[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:459-474.
[73]ZHANG M,WANG Q,XING J,et al.Visual tracking via spatially aligned correlation filters network[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:469-485.
[74]ZHANG Z,PENG H.Deeper and wider siamese networks forreal-time visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:4591-4600.
[75]GAO J,ZHANG T,XU C.Graph convolutional tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:4649-4659.
[76]WANG N,SONG Y,MA C,et al.Unsupervised deep tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:1308-1317.
[77]WANG G,LUO C,XIONG Z,et al.Spm-tracker:Series-parallel matching for real-time visual object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:3643-3652.
[78]LI P,CHEN B,OUYANG W,et al.Gradnet:Gradient-guided network for visual object tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:6162-6171.
[79]YAN B,ZHAO H,WANG D,et al.'Skimming-Perusal' Tracking:A Framework for Real-Time and Robust Long-term Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:2385-2393.
[80]HUANG Z,FU C,LI Y,et al.Learning aberrance repressed correlation filters for real-time uav tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:2891-2900.
[81]HUANG L,ZHAO X,HUANG K.Bridging the gap betweendetection and tracking:A unified approach[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:3999-4009.
[82]WANG G,LUO C,SUN X,et al.Tracking by instance detection:A meta-learning approach[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:6288-6297.
[83]YANG T,XU P,HU R,et al.ROAM:Recurrently Optimizing Tracking Model[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:6718-6727.
[84]LI Y,FU C,DING F,et al.AutoTrack:Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11923-11932.
[85]CHEN Z,ZHONG B,LI G,et al.Siamese Box Adaptive Network for Visual Tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:6668-6677.
[86]YU Y,XIONG Y,HUANG W,et al.Deformable Siamese Attention Networks for Visual Object Tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:6728-6737.
[87]DOSOVITSKIY A,FISCHER P,ILG E,et al.Flownet:Learning optical flow with convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:2758-2766.
[88]ILG E,MAYER N,SAIKIA T,et al.Flownet 2.0:Evolution of optical flow estimation with deep networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2462-2470.
[89]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[90]SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Modeling relational data with graph convolutional networks[C]//European Semantic Web Conference.Springer,Cham,2018:593-607.
[91]RADFORD A,METZ L,CHINTALA S.Unsupervised representation learning with deep convolutional generative adversarial networks[J].arXiv:1511.06434,2015.
[92]DOSOVITSKIY A,SPRINGENBERG J T,RIEDMILLER M,et al.Discriminative unsupervised feature learning with convolutional neural networks[C]//Advances in Neural Information Processing Systems.2014:766-774.
[93]FINN C,ABBEEL P,LEVINE S.Model-agnostic meta-learning for fast adaptation of deep networks[J].arXiv:1703.03400,2017.
[94]YANG T,CHAN A B.Learning dynamic memory networks for object tracking[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:152-167.
[95]ZHU C,HE Y,SAVVIDES M.Feature selective anchor-freemodule for single-shot object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:840-849.
[96]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[97]DUCHI J,HAZAN E,SINGER Y.Adaptive subgradient methods for online learning and stochastic optimization[J].Journal of machine learning research,2011,12(7):2121-2159.
[98]NEBEHAY G,PFLUGFELDER R.Clustering of static-adaptive correspondences for deformable object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:2784-2791.
[99]HONG Z,CHEN Z,WANG C,et al.Multi-store tracker (muster):A cognitive psychology inspired approach to object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:749-758.
[100]DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05).IEEE,2005,1:886-893.
[101]FAN H,LING H.Siamese cascaded region proposal networksfor real-time visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:7952-7961.
[102]WANG Q,ZHANG L,BERTINETTO L,et al.Fast online object tracking and segmentation:A unifying approach[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:1328-1338.
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