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Online Visual Tracking pp 43–60Cite as

Visual Tracking Based on Model Fusion

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Abstract

Model fusion is an essential strategy to tackle the challenges in visual object tracking. Due to the unpredictable appearance changes of the target and background clusters, a single kind of feature or model cannot fit all situations. In this chapter, we introduce two representative methods based on model fusion. The first one is a traditional method focus on how to combine various handcraft features effectively and the second one is a deep-learning-based method which explores the attention information totally in visual tracking.

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Notes

  1. 1.

    ©Reprinted from Neurocomputing, Vol 207, Huilan Jiang, Jianhua Li, Dong Wang, Huchuan Lu, Multi-feature tracking via adaptive weights, Pages No.189-201, Copyright (2016), with permission from Elsevier.

  2. 2.

    ©Reprinted from Pattern Recognition, Vol 87, Boyu Chen, Peixia Li, Chong Sun, Dong Wang, Gang Yang, Huchuan Lu, Multi-attention module for visual tracking, Pages No.80-93, Copyright (2019), with permission from Elsevier.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu & Dong Wang

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  1. Huchuan Lu
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  2. Dong Wang
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Correspondence to Huchuan Lu .

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Lu, H., Wang, D. (2019). Visual Tracking Based on Model Fusion. In: Online Visual Tracking. Springer, Singapore. https://doi.org/10.1007/978-981-13-0469-9_4

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  • DOI: https://doi.org/10.1007/978-981-13-0469-9_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0468-2

  • Online ISBN: 978-981-13-0469-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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