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Visual tracking for UAV using adaptive spatio-temporal regularized correlation filters
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DOI：10.1007/s10489-021-02825-1.png)
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摘要
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The advance of visual tracking has provided unmanned aerial vehicle (UAV) with the intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter (DCF)-based trackers have drawn significant attention and undergone remarkable progress. However, the boundary effect and filter degradation remain two intractable problems. In this work, we propose a novel Adaptive Spatio-Temporal Regularized Correlation Filters (ASTR-CF) model to address the two problems. The ASTR-CF model simultaneously optimizes the spatial and temporal regularization weights adaptively, and it is optimized by the alternating direction method of multipliers (ADMM) effectively. Extensive experiments on 4 UAV tracking benchmarks have proven the superiority of the proposed ASTR-CF compared with more than 30 state-of-the-art trackers in terms of accuracy and speed.
Keyword:
Visual tracking
Correlation filters
Spatio-temporal regularization
UAV
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