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Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

CVPR 2018 · Video › Video segmentation and tracking · approach: Spatio-temporal predictive modeling · 891 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add temporal regularization to the spatially regularized filterSRDCF-style tracker with hand-crafted features · OTB-2015AUC: +5.4%Helped
Add temporal regularization to the spatially regularized filterSRDCF-style tracker with CNN features · OTB-2015Mean OP: +7.4%Helped
Use temporal regularization instead of multiple-sample training with explicit sample reweightingSRDCF-style tracker · OTB-2015Mean OPHelped
Add spatial regularization (ablation)KCF variant SRDCF(-M) · OTB-2015AUC: +6.7%Helped
Add scale estimation (ablation)KCF variant SRDCF(-MS) · OTB-2015AUC: +3.5%Helped
Couple DCF learning with model updating using multiple samples (ablation)SRDCF(-M) · OTB-2015AUC: +1.6%Helped
Add temporal regularization (ablation)SRDCF(-M) · OTB-2015AUC: +6%Helped
Add temporal regularization (ablation)SRDCF · OTB-2015AUC: +4.4%Helped
Set temporal regularization parameter μ to 0, removing historical information (ablation)STRCF · OTB-2015tracking accuracyHurt
Use temporal regularization on deep featuresDeepSRDCF · VOT-2016EAO: +3.7%Helped
Add temporal regularizationSRDCF · Temple-ColorAUC: +3.6%Helped

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