Research map / Detection and segmentation / Tracking
Staple: Complementary Learners for Real-Time Tracking
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine dense template and colour-histogram responses | Staple · VOT14 | accuracy | Helped |
| Combine dense template and colour-histogram responses | Staple · VOT15 | accuracy | Helped |
| Use dense-response merging rather than interpolating final estimates (ablation) | Staple · OTB-13 | accuracy | Helped |
| Set merge factor α around 0.3 (ablation) | Staple · OTB-13 | accuracy | Helped |
| Set template and histogram learning rates around 0.01 (ablation) | Staple · VOT14 | number of failures | Helped |
| Use HOG cells of size 2 × 2 and fixed area 502 (ablation) | Staple · VOT14 | speed | Helped |
| Reduce patch size to HOG cells of size 2 × 2 and fixed area 502 (ablation) | Staple · VOT14 | number of failures | Hurt |
Closest papers by meaning
- Correlation Tracking via Joint Discrimination and Reliability Learning (CVPR 2018 · similarity 0.88)
- Multi-Cue Correlation Filters for Robust Visual Tracking (CVPR 2018 · similarity 0.88)
- Single-Model and Any-Modality for Video Object Tracking (CVPR 2024 · similarity 0.86)
- Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking (AAAI 2024 · similarity 0.86)
- Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision (NeurIPS 2025 · similarity 0.86)
- OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning (CVPR 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.