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Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals

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  • Published: 04 March 2021
  • Volume 18, pages 311–333, (2021)
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International Journal of Automation and Computing Aims and scope Submit manuscript
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals
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  • Xiao-Qin Zhang  ORCID: orcid.org/0000-0003-0958-72851,
  • Run-Hua Jiang  ORCID: orcid.org/0000-0003-2402-86841,
  • Chen-Xiang Fan  ORCID: orcid.org/0000-0002-8793-17261,
  • Tian-Yu Tong  ORCID: orcid.org/0000-0001-7045-88671,
  • Tao Wang  ORCID: orcid.org/0000-0002-0202-01741 &
  • …
  • Peng-Cheng Huang  ORCID: orcid.org/0000-0003-3695-20221 

  • 2270 Accesses

  • 2 Altmetric

  • Explore all metrics

Abstract

Recently, deep learning has achieved great success in visual tracking tasks, particularly in single-object tracking. This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning. First, we introduce basic knowledge of deep visual tracking, including fundamental concepts, existing algorithms, and previous reviews. Second, we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures. Then, we conclude with the general components of deep trackers. In this way, we systematically analyze the novelties of several recently proposed deep trackers. Thereafter, popular datasets such as Object Tracking Benchmark (OTB) and Visual Object Tracking (VOT) are discussed, along with the performances of several deep trackers. Finally, based on observations and experimental results, we discuss three different characteristics of deep trackers, i.e., the relationships between their general components, exploration of more effective tracking frameworks, and interpretability of their motion estimation components.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61922064 and U2033210), Zhejiang Provincial Natural Science Foundation (Nos. LR17F030001 and LQ19F020005), the Project of Science and Technology Plans of Wenzhou City (Nos. C20170008 and ZG2017016).

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  1. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Xiao-Qin Zhang, Run-Hua Jiang, Chen-Xiang Fan, Tian-Yu Tong, Tao Wang & Peng-Cheng Huang

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Xiao-Qin Zhang received the B.Sc. degree in electronic information science and technology from Central South University, China in 2005, and Ph.D. degree in pattern recognition and intelligent system from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China in 2010. He is currently a professor in Wenzhou University, China. He has published more than 80 papers in international and national journals, and international conferences, including IEEE T-PAMI, IJCV, IEEE T-IP, IEEE T-IE, IEEE T-C, ICCV, CVPR, NIPS, IJCAI, AAAI, etc.

His research interests include pattern recognition, computer vision and machine learning.

Run-Hua Jiang received the B.Sc. degree in network engineering from Department of Information Science, Tianjin University of Finance and Economy, China in 2017. He is currently a graduate student in computer software and theory at College of Computer Science and Artificial Intelligence, Wenzhou University, China.

His research interests include several computer vision tasks, such as image/video restoration, crowd counting, visual understanding, and video question answering.

Chen-Xiang Fan received the B.Sc. degree in information and computing science from Department of Information and Computing Science, Ludong University, China in 2020. He is currently a graduate student majoring in computer software and theory at College of Computer Science and Artificial Intelligence, Wenzhou University, China.

His research interests include machine learning, recommendation system and object tracking.

Tian-Yu Tong is currently an undergraduate student in data science and big data technology at College of Computer Science and Artificial Intelligence, Wenzhou University, China.

His research interests include big data technology, pattern recognition and machine learning.

Tao Wang received the B.Sc. degree in information and computing science from Hainan Normal University, China in 2018. He is currently a graduate student at College of Computer Science and Artificial Intelligence, Wenzhou University, China.

His research interests include several topics in computer vision, such as image/ video quality restoration, adversarial learning, visual tracking, image-to-image translation, reinforcement learning.

Peng-Cheng Huang received the B.Sc. degree in electrical engineering and automation from Department of Modern Science and Technology, China Metrology University, China in 2018. He is currently a graduate student in computer software and theory at College of Computer Science and Artificial Intelligence, Wenzhou University, China.

His research interests include image and video processing, pattern recognition and machine learning.

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Zhang, XQ., Jiang, RH., Fan, CX. et al. Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals. Int. J. Autom. Comput. 18, 311–333 (2021). https://doi.org/10.1007/s11633-020-1274-8

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  • Received: 15 September 2020

  • Accepted: 30 December 2020

  • Published: 04 March 2021

  • Issue Date: June 2021

  • DOI: https://doi.org/10.1007/s11633-020-1274-8

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Keywords

  • Deep learning
  • visual tracking
  • data-invariant
  • data-adaptive
  • general components
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