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
张开华, 樊佳庆, 刘青山
ZHANG Kai-hua, FAN Jia-qing, LIU Qing-shan
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。
中图分类号:
[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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