Unifying Registration based Tracking: A Case Study with Structural\n Similarity
Bibliographic record
Abstract
This paper adapts a popular image quality measure called structural\nsimilarity for high precision registration based tracking while also\nintroducing a simpler and faster variant of the same. Further, these are\nevaluated comprehensively against existing measures using a unified approach to\nstudy registration based trackers that decomposes them into three constituent\nsub modules - appearance model, state space model and search method. Several\npopular trackers in literature are broken down using this method so that their\ncontributions - as of this paper - are shown to be limited to only one or two\nof these submodules. An open source tracking framework is made available that\nfollows this decomposition closely through extensive use of generic\nprogramming. It is used to perform all experiments on four publicly available\ndatasets so the results are easily reproducible. This framework provides a\nconvenient interface to plug in a new method for any sub module and combine it\nwith existing methods for the other two. It can also serve as a fast and\nflexible solution for practical tracking needs due to its highly efficient\nimplementation.\n
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".