Research map / Detection and segmentation / Tracking
Hedged Deep Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine convolutional-layer trackers with adaptive Hedge rather than using standard Hedge (ablation) | HDT · OTB-50 | tracking performance | Helped |
| Use only one convolutional-layer tracker instead of hedging trackers from six layers (ablation) | HDT · OTB-50 | tracking performance | Hurt |
| Use features from progressively deeper convolutional layers (ablation) | single-layer VGG trackers · OTB-50 | tracking performance | Helped |
| Use VGG-10 features alone rather than a hand-crafted-feature tracker (ablation) | VGG-10 · OTB-50 | tracking performance | Hurt |
| Combine early-layer spatial details with later-layer semantic features | HDT · low-resolution sequences | tracking performance | Helped |
| Limit target search to the local image region instead of searching the whole frame | HDT · out-of-view challenge | tracking robustness | Hurt |
Closest papers by meaning
- Learning Multi-Domain Convolutional Neural Networks for Visual Tracking (CVPR 2016 · similarity 0.87)
- STCT: Sequentially Training Convolutional Networks for Visual Tracking (CVPR 2016 · similarity 0.87)
- HIPTrack: Visual Tracking with Historical Prompts (CVPR 2024 · similarity 0.84)
- VastTrack: Vast Category Visual Object Tracking (NeurIPS 2024 · similarity 0.84)
- A Distractor-Aware Memory for Visual Object Tracking with SAM2 (CVPR 2025 · similarity 0.83)
- Is Multiple Object Tracking a Matter of Specialization? (NeurIPS 2024 · similarity 0.83)
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