Research map / Detection and segmentation / Tracking
Towards Dense Object Tracking in a 2D Honeybee Hive
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a recurrent previous-frame representation (ablation) | Orientation network · Bee image test set | Orientation angle error (degrees): 34.0 (32.2) -> 15.2 (13.3) | Helped |
| Add a recurrent previous-frame representation (ablation) | Orientation network · Bee image test set | Directed axis error (degrees): 22.1 (16.7) -> 12.1 (9.7) | Helped |
| Add a recurrent previous-frame representation (ablation) | Orientation network · Bee image test set | FP: 0.18 (0.10) -> 0.14 (0.06) | Helped |
| Replace the original U-Net with a reduced network (ablation) | U-Net · Bee image segmentation | Parameter count: 31M -> 1.9M | Helped |
| Use dropout regularization (ablation) | Reduced U-Net · Bee image segmentation and orientation prediction | Overall performance | Hurt |
| Replace foreground class labels with orientation-angle labels and loss (ablation) | Reduced U-Net · Bee image test set | TP: 0.96 -> 0.94 | No clear effect |
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- Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking (AAAI 2020 · similarity 0.84)
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