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Multiple frequency–spatial network for RGBT tracking in the presence of motion blur

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Abstract

RGBT tracking combines visible and thermal infrared images to achieve tracking and faces challenges due to motion blur caused by camera and target movement. In this study, we observe that the tracking in motion blur is significantly affected by both frequency and spatial aspects. And blurred targets exhibit sharp texture details that are represented as high-frequency information. But existing trackers capture low-frequency components while ignoring high-frequency information. To enhance the representation of sharp information in blurred scenes, we introduce multi-frequency and multi-spatial information in network, called FSBNet. First, we construct a modality-specific unsymmetrical architecture and integrate an adaptive soft threshold mechanism into a DCT-based multi-frequency channel attention adapter (DFDA). DFDA adaptively integrates rich multi-frequency information. Second, we propose a masked frequency-based translation adapter (MFTA) to refine drifting failure boxes caused by camera motion. Moreover, we find that small targets get more affected by motion blur compared to larger targets, and we mitigate this issue by designing a cross-scale mutual conversion adapter (CFCA) between the frequency and spatial domains. Extensive experiments on GTOT, RGBT234 and LasHeR benchmarks demonstrate the promising performance of our method in the presence of motion blur.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 41371342) and the National Key Research and Development Program of China (2016YFC0803000)

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  1. School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China

    Shenghua Fan, Xi Chen, Zhongjie Mao & Yujin Zheng

  2. School of Electronic Information, Wuhan University, Wuhan, 430072, Hubei, China

    Chu He & Lei Yu

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Fan, S., Chen, X., He, C. et al. Multiple frequency–spatial network for RGBT tracking in the presence of motion blur. Neural Comput & Applic 35, 24389–24406 (2023). https://doi.org/10.1007/s00521-023-09024-8

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