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Robust cascaded-parallel visual tracking using collaborative color and correlation filter models

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Abstract

In recent years, the multi-expert collaborative tracking strategy has been introduced into visual tracking tasks and achieves impressive performance. Different from most existing multi-expert trackers that linearly fuse multiple tracking models, we propose a novel cascaded-parallel tracking algorithm (CPT) via adaptively selecting the suitable expert among multiple tracking models. And the CPT consists of cascaded and parallel tracking components. In the cascaded tracking component, we hierarchically implement two effective correlation filter models to coarse-to-fine locate the target. And in the parallel tracking component, a color tracking model is applied to locate the target to compensate for the demerit of the correlation filter models. With the proposed adaptive expert selection mechanism, the most reliable expert (i.e. tracking model) is selected for tracking in each frame. Extensive experimental results on OTB2013, OTB2015 and TempleColor128 datasets demonstrate that our proposed algorithm performs favorably against some state-of-the-art algorithms.

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Funding

This work is supported by the National Natural Science Foundation of China [grant number 61972307].

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Authors and Affiliations

  1. School of Mechano-Electronic Engineering, Xidian University, Xi’an, Shaanxi, 710071, People’s Republic of China

    Zhaohui Hao, Guixi Liu, Haoyang Zhang & Fei Wang

  2. School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, Henan, 453003, People’s Republic of China

    Zhaohui Hao

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  1. Zhaohui Hao
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  2. Guixi Liu
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  3. Haoyang Zhang
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  4. Fei Wang
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Correspondence to Guixi Liu.

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Hao, Z., Liu, G., Zhang, H. et al. Robust cascaded-parallel visual tracking using collaborative color and correlation filter models. Multimed Tools Appl 83, 33–59 (2024). https://doi.org/10.1007/s11042-023-15614-4

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  • DOI: https://doi.org/10.1007/s11042-023-15614-4

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