Research map / Video / Video segmentation and tracking
Automatic Fence Segmentation in Videos of Dynamic Scenes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add dense CRF refinement after graph-cut segmentation (ablation) | Automatic Fence Segmentation · 18-video dataset (average) | precision (%): 73.81 -> 80.92 | Helped |
| add dense CRF refinement after graph-cut segmentation (ablation) | Automatic Fence Segmentation · 18-video dataset (average) | recall (%): 69.86 -> 82.31 | Helped |
| use the proposed automatic segmentation instead of Rotobrush | Automatic Fence Segmentation · 18-video dataset (average) | precision (%): 57.43 -> 80.92 | Helped |
| use the proposed automatic segmentation instead of Rotobrush | Automatic Fence Segmentation · 18-video dataset (average) | recall (%): 52.25 -> 82.31 | Helped |
| use the proposed method instead of method [21] | Automatic Fence Segmentation · 18-video dataset (average) | precision (%): 46.34 -> 80.92 | Helped |
| use the proposed method instead of method [21] | Automatic Fence Segmentation · 18-video dataset (average) | recall (%): 69.61 -> 82.31 | Helped |
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