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Context and saliency aware correlation filter for visual tracking

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Abstract

Visual tracking in complex scenarios is a big challenge in the computer vision community. Due to correlation filter (CF) recently have achieved excellent results both on accuracy and robustness in visual tracking, many researchers have focused on incorporating different features for better represent the tracking target. However, CF-based trackers have poor ability to handle problem in many complex scenes with challenges like deformation, motion blur and background clutters. To overcome these defects, we propose a context and saliency aware CF for visual tracking (CSCF). Context information around the target of interest is introduced into correlation filters to strengthen the discriminative ability of CF, which can reduce the boundary effect and the influence of the background. Then the saliency feature map of the target is combined with CF to strengthen the ability to extract targets of interest from complex background. Experimental results show that the proposed method shows competitive performance on OTB dataset and UAV dataset compared to several other CF trackers.

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All the data used to support the findings of this study are included within the article.

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Funding

This work was supported by National Natural Science Foundation of China (Grant No. 61972068, 61976042), LiaoNing Revitalization Talents Program (Grant No. XLYC2007023), Wuhan Chegu Industrial Talents Program, Innovative Talents Program for Liaoning Universities (Grant No. LR2019020).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, China

    Fasheng Wang, Shuangshuang Yin & Fuming Sun

  2. Department of Computer Science and Engineering, University of Dar es salaam, Da es salaam, 33335, Tanzania

    Jimmy T. Mbelwa

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  1. Fasheng Wang
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  2. Shuangshuang Yin
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  3. Jimmy T. Mbelwa
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  4. Fuming Sun
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Correspondence to Fuming Sun.

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Wang, F., Yin, S., Mbelwa, J.T. et al. Context and saliency aware correlation filter for visual tracking. Multimed Tools Appl 81, 27879–27893 (2022). https://doi.org/10.1007/s11042-022-12760-z

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  • DOI: https://doi.org/10.1007/s11042-022-12760-z

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