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Visible and infrared tracking based on multi-view multi-kernel fusion model

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Abstract

In the visual tracking problem, fusion of visible and infrared sensors provides complementarily useful features and can consistently help distinguish the target from the background efficiently. Recently, multi-view learning has received growing attention due to its enormous potential in combining diverse view features containing consistent and complementary characteristics. Therefore, in this paper, a visible and infrared fusion tracking algorithm based on multi-view multi-kernel fusion (MVMKF) model is presented. The proposed MVMKF model considers the diversities of visible and infrared views and embeds complementary information from them. Furthermore, the multi-kernel framework is used to learn the importance of view features so that an integrated appearance representation is made with regard to the respective performance. Besides, the tracking task is completed with naive Bayes classifier in sophisticated compressive feature domain, considering the high performances of classifier-level and sophisticated feature-level learning for multiple views. The experimental results demonstrate that the MVMKF tracking algorithm performs well in terms of accuracy and robustness.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61175028, 61365009) and the Ph.D. Programs Foundation of the Ministry of Education of China (Grant Nos. 20090073110045).

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiao Yun & Zhongliang Jing

  2. Software Engineering Institute, East China Normal University, Shanghai, 200062, China

    Bo Jin

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  1. Xiao Yun
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  2. Zhongliang Jing
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  3. Bo Jin
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Correspondence to Xiao Yun.

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Yun, X., Jing, Z. & Jin, B. Visible and infrared tracking based on multi-view multi-kernel fusion model. Opt Rev 23, 244–253 (2016). https://doi.org/10.1007/s10043-015-0175-5

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