J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2024 , Vol. 29 >Issue 3: 400 - 413

DOI: https://doi.org/10.1007/s12204-022-2484-8

Automation & Computer Technologies

Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction

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  • (1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Shanghai Aerospace Control Technology Institute, Shanghai 201109, China)

Accepted date: 2021-09-11

  Online published: 2024-05-28

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Abstract

Visual object tracking is an important issue that has received long-term attention in computer vision.The ability to effectively handle occlusion, especially severe occlusion, is an important aspect of evaluating theperformance of object tracking algorithms in long-term tracking, and is of great significance to improving therobustness of object tracking algorithms. However, most object tracking algorithms lack a processing mechanism specifically for occlusion. In the case of occlusion, due to the lack of target information, it is necessary to predict the target position based on the motion trajectory. Kalman filtering and particle filtering can effectively predict the target motion state based on the historical motion information. A single object tracking method, called probabilistic discriminative model prediction (PrDiMP), is based on the spatial attention mechanism in complex scenes and occlusions. In order to improve the performance of PrDiMP, Kalman filtering, particle filtering and linear filtering are introduced. First, for the occlusion situation, Kalman filtering and particle filtering are respectively introduced to predict the object position, thereby replacing the detection result of the original tracking algorithm and stopping recursion of target model. Second, for detection-jump problem of similar objects in complex scenes, a linear filtering window is added. The evaluation results on the three datasets, including GOT-10k, UAV123 and LaSOT, and the visualization results on several videos, show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed.

Cite this article

CHEN Kun(陈坤), ZHAO Xu(赵旭), DONG Chunyu(董春玉), DI Zichao(邸子超), CHEN Zongzhi(陈宗枝) . Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction[J]. Journal of Shanghai Jiaotong University(Science), 2024 , 29(3) : 400 -413 . DOI: 10.1007/s12204-022-2484-8

References

[1] LU H C, LI P X, WANG D. Visual object tracking: A survey [J]. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 61-76 (in Chinese).
[2] BABENKO B, YANG M H, BELONGIE S. Robust object tracking with online multiple instance learning [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
[3] KALAL Z, MIKOLAJCZYK K, MATAS J. Trackinglearning-detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
[4] AVIDAN S. Support vector tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064-1072.
[5] HARE S, GOLODETZ S, SAFFARI A, et al. Struck: Structured output tracking with kernels [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096-2109.
[6] SAFFARI A, LEISTNER C, SANTNER J, et al. Online random forests [C]//2009 IEEE 12th International Conference on Computer Vision Workshops. Kyoto: IEEE, 2009: 1393-1400.
[7] BABENKO B, YANG M H, BELONGIE S. Visual tracking with online multiple instance learning [C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL: IEEE, 2009: 983-990.
[8] JIANG N, LIU W Y, WU Y. Learning adaptive metric for robust visual tracking [J]. IEEE Transactions on Image Processing, 2011, 20(8): 2288-2300.
[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. San Francisco, CA: IEEE, 2010: 2544-2550.
[10] BHAT G, DANELLJAN M, VAN GOOL L, et al. Learning discriminative model prediction for tracking [C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 6181-6190.
[11] DANELLJAN M, VAN GOOL L, TIMOFTE R. Probabilistic regression for visual tracking [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA: IEEE, 2020: 7181-7190.
[12] HAN G, DU H, LIU J X, et al. Fully conventional anchor-free Siamese networks for object tracking [J]. IEEE Access, 2019, 7: 123934-123943.
[13] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional Siamese networks for object tracking [M]//Computer vision – ECCV 2016 Workshops. Cham: Springer, 2016: 850-865.
[14] LI B, YAN J J, WU W, et al. High performance visual tracking with Siamese region proposal network [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 8971-8980.
[15] WANG Q, TENG Z, XING J L, et al. Learning attentions: residual attentional Siamese network for high performance online visual tracking [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 4854-4863.
[16] LI B, WU W, WANG Q, et al. SiamRPN++: Evolution of Siamese visual tracking with very deep networks [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: IEEE, 2019: 4277-4286.
[17] FAN H, LING H B. Siamese cascaded region proposal networks for real-time visual tracking [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: IEEE, 2019: 7944-7953.
[18] GUO D Y, WANG J, CUI Y, et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA: IEEE, 2020: 6268-6276.
[19] XU Y D, WANG Z Y, LI Z X, et al. SiamFC++: towards robust and accurate visual tracking with target estimation guidelines [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12549-12556.
[20] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module [M]//Computer vision – ECCV 2018. Cham: Springer, 2018: 3-19.
[21] KALMAN R E. A new approach to linear filtering and prediction problems [J]. Journal of Basic Engineering, 1960, 82(1): 35-45.
[22] ISARD M, BLAKE A. Condensation: Conditional density propagation for visual tracking [J]. International Journal of Computer Vision, 1998, 29: 5-28.
[23] HUANG L H, ZHAO X, HUANG K Q. GOT-10k: A large high-diversity benchmark for generic object tracking in the wild [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5): 1562-1577.
[24] MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking [M]//Computer vision — ECCV 2016. Cham: Springer, 2016: 445-461.
[25] FAN H, LIN L T, YANG F, et al. LaSOT: A highquality benchmark for large-scale single object tracking [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: IEEE, 2019: 5369-5378.
[26] JIANG B, LUO R, MAO J, et al. Acquisition of localization confidence for accurate object detection [M]//Computer vision — ECCV 2018. Cham: Springer, 2018: 784-799.
[27] DANELLJAN M, BHAT G, KHAN F S, et al. ATOM: Accurate tracking by overlap maximization [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA: IEEE, 2019: 4655-4664.
[28] WANG Q, GAO J, XING J L, et al. DCFNet: Discriminant correlation filters network for visual tracking [EB/OL]. (2017-04-13). https://arxiv.org/abs/1704.04057.
[29] DANELLJAN M, H¨AGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking [C]//2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 4310-4318.
[30] DANELLJAN M, H¨AGER G, KHAN F S, et al. Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016: 1430-1438.
[31] DANELLJAN M, BHAT G, KHAN F S, et al. ECO: efficient convolution operators for tracking [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 6931-6939.
[32] WU Y, LIM J, YANG M H. Online object tracking: A benchmark [C]//2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR: IEEE, 2013: 2411-2418.
[33] KRISTAN M, LEONARDIS A, MATAS J, et al. The sixth visual object tracking VOT2018 challenge results [M]//Computer vision – ECCV 2018 Workshops. Cham: Springer, 2019: 3-53.
[34] CHEN Z. Bayesian filtering: From Kalman filters to particle filters, and beyond [J]. Statistics, 2003, 182(1): 1-69.
[35] MARSHALL A W. The use of multi-stage sampling schemes in Monte Carlo computations [R]. New York: Rand Corp Santa Monica Calif, 1954.
[36] HANDSCHIN J E, MAYNE D Q. Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering [J]. International Journal of Control, 1969, 9(5): 547-559.
[37] KONG A, LIU J S, WONG W H. Sequential imputations and Bayesian missing data problems [J]. Journal of the American Statistical Association, 1994, 89(425): 278-288.
[38] KITAGAWA G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models [J]. Journal of Computational and Graphical Statistics, 1996, 5(1): 1-25.
[39] DEL MORAL P, JACOD J, PROTTER P. The Monte-Carlo method for filtering with discrete-time observations [J]. Probability Theory and Related Fields, 2001, 120(3): 346-368.
[40] CARPENTER J, CLIFFORD P, FEARNHEAD P. Improved particle filter for nonlinear problems [J]. IEE Proceedings-Radar, Sonar and Navigation, 1999, 146(1): 2.
[41] CARPENTER J, CLIFFORD P, FEARNHEAD P. Building robust simulation-based filters for evolving data sets [R]. Oxford: University of Oxford, 1999.
[42] KRISTAN M, MATAS J, LEONARDIS A, et al. The seventh visual object tracking VOT2019 challenge results [C]//2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul: IEEE, 2019: 2206-2241.
[43] DANELLJAN M, ROBINSON A, SHAHBAZ KHAN F, et al. Beyond correlation filters: Learning continuous convolution operators for visual tracking [M]//Computer vision — ECCV 2016. Cham: Springer, 2016: 472-488.
[44] HELD D, THRUN S, SAVARESE S. Learning to track at 100 FPS with deep regression networks [M]//Computer vision — ECCV 2016. Cham: Springer, 2016: 749-765.
[45] VALMADRE J, BERTINETTO L, HENRIQUES J, et al. End-to-end representation learning for correlation filter based tracking [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 5000-5008.
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