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A survey on online learning for visual tracking

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Abstract

Visual object tracking has become one of the most active research topics in computer vision, which has been growing in commercial development as well as academic research. Many visual trackers have been proposed in the last two decades. Recent studies of computer vision for dynamic scenes include motion detection, object classification, environment modeling, tracking of moving objects, understanding of object behaviors, object identification, and data fusion from multiple sensors. This paper provides an in-depth overview of recent object tracking research. Object tracking tasks in realistic scenario often face challenging problems such as camera motion, occlusion, illumination effect, clutter, and similar appearance. A variety of tracker techniques have been published, which combine multiple techniques to solve multiple visual tracking sub-problems. This paper also reviews the latest research trend in object tracking based on convolutional neural networks, which is receiving growing attention. Finally, the paper discusses the future challenges and research directions for the object tracking problems that still need extensive studies in coming years.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) (No. NRF-2018R1D1A3B07044041), under the ITRC (Information Technology Research Center) support program supervised by the IITP (Institute for Information & communications Technology Promotion) (IITP-2020-2015-0-00448), and under Industrial Technology Innovation Program (No.20002655), grant funded by Korea Government.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Chungbuk National University, Cheongju, 28644, South Korea

    Mohammed Y. Abbass, Ki-Chul Kwon & Nam Kim

  2. Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt

    Mohammed Y. Abbass & Safey A. Abdelwahab

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menoufia, 32952, Egypt

    Fathi E. Abd El-Samie

  4. Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt

    Ashraf A. M. Khalaf

  5. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Mohammed Y. Abbass
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  2. Ki-Chul Kwon
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  3. Nam Kim
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  4. Safey A. Abdelwahab
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  5. Fathi E. Abd El-Samie
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  6. Ashraf A. M. Khalaf
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Correspondence to Nam Kim.

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Abbass, M.Y., Kwon, KC., Kim, N. et al. A survey on online learning for visual tracking. Vis Comput 37, 993–1014 (2021). https://doi.org/10.1007/s00371-020-01848-y

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