[1] Li X, Zha Y F, Zhang T Z et al. Survey of visual object tracking algorithms based on deep learning[J]. Journal of Image and Graphics, 24, 2057-2080(2019).
[2] Chen T. Moving objects tracking in aerial videos[D](2018).
[3] Xue C, Zhu M, Liu C X. Review of tracking algorithms under occlusions[J]. Chinese Journal of Optics and Applied Optics, 2, 388-394(2009).
[4] Meng L, Yang X. A survey of object tracking algorithms[J]. Acta Automatica Sinica, 45, 1244-1260(2019).
[5] Ge B Y, Zuo X Z, Hu Y J. Review of visual object tracking technology[J]. Journal of Image and Graphics, 23, 1091-1107(2018).
[6] Marvasti-Zadeh S M, Cheng L, Ghanei-Yakhdan H et al. Deep learning for visual tracking: a comprehensive survey[EB/OL]. https://arxiv.org/abs/1912.00535
[7] Wang N Y, Shi J P, Yeung D Y et al. Understanding and diagnosing visual tracking systems[C], 3101-3109(2015).
[8] Bolme D S, Beveridge J R, Draper B A et al. Visual object tracking using adaptive correlation filters[C], 2544-2550(2010).
[9] Henriques J F, Caseiro R, Martins P et al. Exploiting the circulant structure of tracking-by-detection with kernels[M]. Fitzgibbon A, Lazebnik S, Perona P, et al. Computer vision-ECCV 2012. Lecture notes in computer science, 7575, 702-715(2012).
[10] Danelljan M, Khan F S, Felsberg M et al. Adaptive color attributes for real-time visual tracking[C], 1090-1097(2014).
[11] Vojir T, Noskova J, Matas J. Robust scale-adaptive mean-shift for tracking[J]. Pattern Recognition Letters, 49, 250-258(2014).
[12] Possegger H, Mauthner T, Bischof H. In defense of color-based model-free tracking[C], 2113-2120(2015).
[13] Fan J Q, Song H H, Zhang K H et al. Complementary tracking via dual color clustering and spatio-temporal regularized correlation learning[J]. IEEE Access, 6, 56526-56538(2018).
[14] 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, 37, 583-596(2015).
[15] Galoogahi H K, Fagg A, Lucey S. Learning background-aware correlation filters for visual tracking[C], 1144-1152(2017).
[16] Ma C, Yang X K, Zhang C Y et al. Long-term correlation tracking[C], 5388-5396(2015).
[17] Danelljan M, Häger G, Shahbaz Khan F et al. Accurate scale estimation for robust visual tracking[C](2014).
[18] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409-1422(2012).
[19] Zhu Z, Wu W, Zou W et al. End-to-end flow correlation tracking with spatial-temporal attention[C], 548-557(2018).
[20] Danelljan M, Häger G, Khan F S et al. Convolutional features for correlation filter based visual tracking[C], 621-629(2015).
[21] Valmadre J, Bertinetto L, Henriques J et al. End-to-end representation learning for correlation filter based tracking[C], 5000-5008(2017).
[22] Liu M, Wu C D, Zhang Y Z. Motion vehicle tracking based on multi-resolution optical flow and multi-scale Harris corner detection[C], 2032-2036(2007).
[23] Wu Y, Li L F, Xiao Z S et al. Optical flow motion tracking algorithm based on SIFT feature[J]. Computer Engineering and Applications, 49, 157-161(2013).
[24] Bouguet J Y. Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm[J]. Intel Corporation, 5, 4(2001).
[25] Liu P P, King I, Lyu M R et al. DDFlow: learning optical flow with unlabeled data distillation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 8770-8777(2019).
[26] Hur J, Roth S. MirrorFlow: exploiting symmetries in joint optical flow and occlusion estimation[C], 312-321(2017).
[27] Li H Y, Bi D Y, Yang Y et al. Research on visual tracking algorithm based on deep feature expression and learning[C], 8770-8777(2015).
[28] Bhat G, Johnander J, Danelljan M et al. Unveiling the power of deep tracking[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11206, 493-509(2018).
[29] Li Y, Zhu J K. A scale adaptive kernel correlation filter tracker with feature integration[M]. Agapito L, Bronstein M M, Rother C. Computer vision-ECCV 2014 workshops. Lecture notes in computer science, 8926, 254-265(2015).
[30] Zhu G B, Wang J Q, Wu Y et al. MC-HOG correlation tracking with saliency proposal[C], 3690-3696(2016).
[31] Bertinetto L, Valmadre J, Golodetz S et al. Staple: complementary learners for real-time tracking[C], 1401-1409(2016).
[32] Bouachir W, Bilodeau G A. Structure-aware keypoint tracking for partial occlusion handling[C], 877-884(2014).
[33] Akin O, Erdem E, Erdem A et al. Deformable part-based tracking by coupled global and local correlation filters[J]. Journal of Visual Communication and Image Representation, 38, 763-774(2016).
[34] Ma C Y, Yu G. An improved kernel correlation filter for occlusion target tracking[C], 674-678(2019).
[35] Li X, Liu Q, He Z Y et al. A multi-view model for visual tracking via correlation filters[J]. Knowledge-Based Systems, 113, 88-99(2016).
[36] Liu H F, Sun C, Liang X L. Correlation-filter tracking algorithm with adaptive-feature fusion and anti-occlusion[J]. Laser & Optoelectronics Progress, 57, 141014(2020).
[37] Ma C, Huang J B, Yang X K et al. Hierarchical convolutional features for visual tracking[C], 3074-3082(2015).
[38] Danelljan M, Robinson A, Khan F S et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9909, 472-488(2016).
[39] Danelljan M, Bhat G, Khan F S et al. ECO: efficient convolution operators for tracking[C], 6931-6939(2017).
[40] Li Y, Zhang Y F, Xu Y L et al. Robust scale adaptive kernel correlation filter tracker with hierarchical convolutional features[J]. IEEE Signal Processing Letters, 23, 1136-1140(2016).
[41] Liu P D, Yan X Y, Jiang Y et al. Deep flow collaborative network for online visual tracking[C], 2598-2602(2020).
[42] Zhang P, Zhuo T, Huang W et al. Online object tracking based on CNN with spatial-temporal saliency guided sampling[J]. Neurocomputing, 257, 115-127(2017).
[43] Jiang X L, Zhen X T, Zhang B C et al. Deep collaborative tracking networks[C], 87(2018).
[44] Fan H, Ling H B. SANet: structure-aware network for visual tracking[C], 2217-2224(2017).
[45] Ma D, Bu W, Wu X. Multi-scale recurrent tracking via pyramid recurrent network and optical flow[C], 242(2018).
[46] Hu P. Research on video object tracking with Kalman filter[D](2010).
[47] Heimbach M, Ebadi K, Wood S. Resolving occlusion ambiguity by combining Kalman tracking with feature tracking for image sequences[C], 144-147(2017).
[48] Heimbach M, Ebadi K, Wood S. Improving object tracking accuracy in video sequences subject to noise and occlusion impediments by combining feature tracking with Kalman filtering[C], 1499-1502(2018).
[49] Mehmood K, Jalil A, Ali A et al. Context-aware and occlusion handling mechanism for online visual object tracking[J]. Electronics, 10, 43(2020).
[50] Sun Y J, Zhang L Y, Yun X. Visual tracking algorithm based on region estimation and adaptive classification[J]. Laser & Optoelectronics Progress, 56, 181001(2019).
[51] Li Y B, Jiu M Y, Sun Q et al. An improved target tracking algorithm based on extended Kalman filter for UAV[C], 435-437(2018).
[52] Song J S, Hu G Q. Multi-feature visual tracking using adaptive unscented Kalman filtering[C], 197-200(2013).
[53] Ahmed E, Ahmad A, Hadhoud M. A robust framework for object tracking based on corrected background-weighted histogram mean shift and unscented Kalman filter[C], 14947318(2014).
[54] Duan Z H, Cai Z X. Adaptive evolutionary particle filter based object tracking with occlusion handling[C], 358-361(2009).
[55] Liu K J, Liu B, Yu N H. A robust occlusion judgment scheme for target tracking under the framework of particle filter[M]. Zhang Y J. Image and graphics. Lecture notes in computer science, 9217, 423-433(2015).
[56] Li L Q, Yan M Y. High-order cumulant-based particle filtering algorithm for pedestrian object tracking[C], 304-307(2018).
[57] Wen W, Wu L Z. Tracking algorithm of improved spatio-temporal context with particle filter[C], 1549-1553(2017).
[58] Guo C J, Lu Y, Fang X Z et al. Multiple likelihoods and state noises based particle filter for long-lived full occlusion handling[C], 11582115(2010).
[59] Zhang T, Fei S M. Improved particle filter for object tracking[C], 3586-3590(2011).
[60] Qi Y J, Wang Y J, Liu Y C. Object tracking based on deep CNN feature and color feature[C], 469-473(2018).
[61] Daneshyar M A, Nahvi M. Improvement of moving objects tracking via modified particle distribution in particle filter algorithm[C], 15323867(2015).
[62] Zhang S P, Wu J P, Tian Y et al. Robust visual tracking based on occlusion detection and particle redistribution[C], 159-162(2010).
[63] Qi Y J, Wang Y J. Memory-based state estimation for handling occlusion during object tracking by particle filter[C], 953-957(2011).
[64] Zhai Y Y, Song P, Mou Z L et al. Occlusion-aware correlation particle filter target tracking based on RGBD data[J]. IEEE Access, 6, 50752-50764(2018).
[65] Meshgi K, Maeda S I, Oba S et al. An occlusion-aware particle filter tracker to handle complex and persistent occlusions[J]. Computer Vision and Image Understanding, 150, 81-94(2016).
[66] Han Y X, Ding G Y. Occlusion target tracking based on particle filter and neural network[J]. Computer Integrated Manufacturing Systems, 26, 3229-3235(2020).
[67] Cao W M, Li Y H, He Z H et al. Supplementary virtual keypoints of weight-based correspondences for occluded object tracking[J]. IEEE Access, 6, 9140-9146(2018).
[68] Cui Z, Xiao S T, Feng J S et al. Recurrently target-attending tracking[C], 1449-1458(2016).
[69] Du X F, Dore A, Stoyanov D. Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking[EB/OL]. https://arxiv.org/abs/1708.01179
[70] Yang J F, Zhang J P. Long time target tracking based on kernel correlation filtering[J]. Laser & Optoelectronics Progress, 56, 021502(2019).
[71] Li Y, Zhu J K, Hoi S C H. Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches[C], 353-361(2015).
[72] Fan H, Xiang J. Robust visual tracking via local-global correlation filter[C], 4025-4031(2017).
[73] Ondruska P, Posner I. Deep tracking: seeing beyond seeing using recurrent neural networks[C], 3361-3368(2016).
[74] Ning G H, Zhang Z, Huang C et al. Spatially supervised recurrent convolutional neural networks for visual object tracking[C], 17208133(2017).
[75] Gordon D, Farhadi A, Fox D. Re3: real-time recurrent regression networks for visual tracking of generic objects[J]. IEEE Robotics and Automation Letters, 3, 788-795(2018).
[76] Kosiorek A R, Bewley A, Posner I. Hierarchical attentive recurrent tracking[EB/OL]. https://arxiv.org/abs/1706.09262
[77] Wen L Y, Cai Z W, Lei Z et al. Online spatio-temporal structural context learning for visual tracking[M]. Fitzgibbon A, Lazebnik S, Perona P, et al. Computer vision-ECCV 2012. Lecture notes in computer science, 7575, 716-729(2012).
[78] Zhang K H, Zhang L, Liu Q S et al. Fast visual tracking via dense spatio-temporal context learning[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 127-141(2014).
[79] Mueller M, Smith N, Ghanem B. Context-aware correlation filter tracking[C], 1387-1395(2017).
[80] Bhat G, Danelljan M, van Gool L et al. Know your surroundings: exploiting scene information for object tracking[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12368, 205-221(2020).
