Real-Time Edge Template Tracking via Homography Estimation
Bibliographic record
Abstract
In this paper, we propose a novel real-time method for tracking planar edge templates. This method tracks an edge template by estimating its homography transformations with respect to the sampled edge pixels detected from the incoming frames. Particularly, we define a cost function based on a new feature map of the to-be-tracked edge template and optimize it by a Lucas-Kanade-like algorithm. The feature map is defined as the fourth root of the distance transform. Our method operates on just edges so that it is good at tracking those low textured targets, such as hollow targets (mug rim), thin targets (cable, ring) and non-Lambertian objects (disc). We validate and compare our method with four other methods on five newly collected real-world video sequences. The results achieves the lowest overall average error (1.58 pixels) and also outperforms others in terms of success rate. The per frame processing time of about 30 ms proves that our method is acceptable in realtime applications. The code and dataset are publicly available at: http://webdocs.cs.ualberta.ca/~xuebin/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".