KSII Transactions on Internet and Information Systems (TIIS)

  • 제18권8호
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  • Pages.2067-2081
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  • 2024
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Meta Learning based Object Tracking Technology: A Survey

  • Ji-Won Baek (Department of Computer Science, Kyonggi University) ;
  • Kyungyong Chung (Division of AI Computer Science and Engineering, Kyonggi University)
  • 투고 : 2023.12.07
  • 심사 : 2024.07.19
  • 발행 : 2024.08.31
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초록

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.

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과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2022R1F1A1068828). Additionally, this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2020R1A6A1A03040583).

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