Research map / Detection and segmentation / Tracking
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly learn appearance-model parameters and continuous training-sample weights | SRDCF · OTB-2015 | mean OP (%): 72.9 -> 76.7 | Helped |
| Use entropy-based expert management instead of joint sample-weight learning | SRDCF · OTB-2015 | mean OP (%): 76.7 vs 72.2 | Hurt |
| Use PSR-based sample management instead of joint sample-weight learning | SRDCF · OTB-2015 | mean OP (%): 76.7 vs 74.4 | Hurt |
| Jointly learn appearance-model parameters and continuous training-sample weights | SRDCF · OTB-2015 | AUC (%): 60.5% -> 63.4% | Helped |
| Jointly learn appearance-model parameters and continuous training-sample weights | Linear SVM tracker · OTB-2015 | mean OP (%): 58.2% -> 61.4% | Helped |
| Jointly learn appearance-model parameters and continuous training-sample weights | SRDCF · VOT-2015 | AEO: 0.288 -> 0.299 | Helped |
| Jointly learn appearance-model parameters and continuous training-sample weights | SRDCF · Temple-Color | mean OP (%): 62.2% -> 65.8% | Helped |
| Jointly learn appearance-model parameters and continuous training-sample weights | SRDCF · Temple-Color | AUC (%): 51.6% -> 54.1% | Helped |
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