Channel-Weighted Structured Correlation Filters for UAV Tracking

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Abstract
In the visual object tracking via unmanned aerial vehicle (UAV) the correlation filtering (CF) is one of the mainstream methods for the reason that optimizing filters by circulant samples facilitates the calculation. The prevalent discriminative CF (DCF) frameworks credited to a ridge regression model followed by various regularizations focus on regressing to a fixed Gaussian label, which maybe easily induce over-fitting. To these concerns, we integrate the hinge-squared loss (HSL) of structured SVM with the CF model so as to attain a dynamical label which regresses the difference between target and background samples and thus enhances the robustness in tracking. Moreover, we assume weighted channels to augment the discrimination of hinge-squared loss, thus indicating it has as good extensibility as the ridge regression in usual CF frameworks. The proposed method channel-weighted structured correlation filtering (CWSCF) has achieved excellent performance compared with other methods in mainstream UAV tracking benchmarks.
Funding Information
  • National Natural Science Foundation of China (51935005)
  • Natural Science Foundation of Heilongjiang Province, China (LH2021F023)
  • Basic Scientific Research Program (JCKY20200603C010)

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