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A survey of appearance models in visual object tracking
Open accessAbstract
Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Research works printed but not linked: 5
- 10.5555/794191.794734 · DOI
- 10.5555/521384.836819 · DOI
- 10.5555/645315.649157 · DOI
- 10.5555/1018428.1020821 · DOI
- Visentini , I. , Snidaro , L. , and Foresti , G. L . 2008. Dynamic ensemble for target tracking . In Proceedings of the International Workshop on Visual Surveillance. Visentini, I., Snidaro, L., and Foresti, G. L. 2008. Dynamic ensemble for target tracking. In Proceedings of the International Workshop on Visual Surveillance.
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Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371
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Ma, C., Huang, J., Yang, X., & Yang, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.352 · Cited by 1,819
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BackgroundTrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
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MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization
Zhang, J., Ma, S., & Sclaroff, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_13 · Cited by 1,150
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Zhang, Z., Peng, H., Fu, J., Li, B., & Hu, W. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58589-1_46 · Cited by 894
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Teerapittayanon, S., McDanel, B., & Kung, H. T. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.226 · Cited by 843
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., & Yang, M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.466 · Cited by 735
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Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. Č., Vojír, T., Häger, G., Lukežič, A., Eldesokey, A., et al. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.79 · Cited by 895
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Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., & Maybank, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00510 · Cited by 604
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Zitouni, M., Bhaskar, H., Dias, J., & Al-Mualla, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.070 · Cited by 98
Liu, Y., Jing, X., Nie, J., Gao, H., Liu, J., & Jiang, G. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2863604 · Cited by 103
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Zhang, S., Wang, C., Chan, S. C., Wei, X., & Ho, C. S. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2382174 · Cited by 94
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Guo, M., Zhang, Z., Fan, H., Jing, L., Lyu, Y., Li, B., & Hu, W. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/130 · Cited by 73
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Felsberg, M., Kristan, M., Matas, J., Leonardis, A., Pflugfelder, R. P., Hager, G., Berg, A., Eldesokey, A., Ahlberg, J., Zajc, L. Č., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_55 · Cited by 72
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Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation
Wan, M., Gu, G., Qian, W., Ren, K., Maldague, X., & Chen, Q. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2930656 · Cited by 62
Deep learning to frame objects for visual target tracking
Pang, S., del Coz, J. J., Yu, Z., Luaces, O., & Díez, J. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.08.010 · Cited by 60
Camouflaged Object Detection and Tracking: A Survey
Mondal, A. (2020) · International Journal of Image and Graphics · doi:10.1142/s021946782050028x · Cited by 60
Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions
Kim, E.-J., Park, H., Ham, S. W., Kho, S., & Kim, D. (2019) · Journal of Advanced Transportation · doi:10.1155/2019/9060797 · Cited by 60
Collaborative Correlation Tracking
Zhu, G., Wang, J., Wu, Y., & Lu, H. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.184 · Cited by 59
DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object Tracking
Abdelpakey, M. H., Shehata, M., & Mohamed, M. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03801-4_41 · Cited by 57
Generalized Pooling for Robust Object Tracking
Ma, B., Hu, H., Shen, J., Liu, Y., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2588329 · Cited by 56
A novel visual tracking method using bat algorithm
Gao, M., Shen, J., Yin, L., Liu, W., Zou, G., Li, H., & Fu, G. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.11.072 · Cited by 56
Real-time visual tracking by deep reinforced decision making
Choi, J., Kwon, J., & Lee, K. M. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.05.009 · Cited by 90
Tracking Using Multilevel Quantizations
Hong, Z., Wang, C., Mei, X., Prokhorov, D. V., & Tao, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_11 · Cited by 55
When Correlation Filters Meet Convolutional Neural Networks for Visual Tracking
Ma, C., Xu, Y. X., Ni, B., & Yang, X. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2601691 · Cited by 53
Metadata
- Authors
- Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D.
- Affiliations
- Chinese Academy of Sciences (CN)
- The University of Adelaide (AU)
- Binghamton University (US)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2013-09
- Volume
- 4
- Issue
- 4
- Pages
- 1-48
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W1984914017
- Cited by
- 705
- References
- 214
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 15 · 2023: 26 · 2022: 30 · 2021: 50 · 2020: 51 · 2019: 81 · 2018: 98 · 2017: 97 · 2016: 111 · 2015: 83 · 2014: 47 · 2013: 5
- FWCI
- 92.20
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council
- National Natural Science Foundation of China (60935002)
- Ministry of Science and Technology of the People's Republic of China (2012AA012504, 2012CB316400)
- Natural Science Foundation of Beijing Municipality (4121003)
- Division of Information and Intelligent Systems (IIS-0812114, CCF-1017828)
- Division of Computing and Communication Foundations (IIS-0812114, CCF-1017828)
- Guangdong Natural Science Foundation (S2012020011081)
- Zhejiang Provincial Engineering Center onMedia Data Cloud Processing and Analysis
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual object tracking, visual representation, statistical modeling, tracking-by-detection, generative models, discriminative models, hybrid generative-discriminative models, illumination variation, partial occlusion, shape deformation, camera movement
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