Extended cuckoo search‐based kernel correlation filter for abrupt motion tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kernelised correlation filter (KCF)‐based trackers have recently attracted considerable attention due to their exciting accuracy and efficiency. Numerous improvements have been made later for coping with scales variation or partial occlusion etc . However, when there is an abrupt motion between the consecutive image frames, these trackers would face failure. To alleviate the problem, the authors present an extended cuckoo search (CS)‐based KCF tracker (called ECSKCF). At first, the extended CS algorithm is constructed by the Simplex method (SM). CS has obvious capability in global search while the SM has exceptional advantage in local search. Based on ECS method, motion prediction is transformed to globally search for optimal position intending to enhance the quality of base image. Then, combined ECS with Gaussian distribution, a hybrid motion model is introduced to KCF framework, which has the capability of capturing abrupt motion. Finally, a unified framework is designed to track smooth or abrupt motion simultaneously. Extensive experimental results in both quantitative and qualitative measures demonstrate the effectiveness of the authors’ proposed method for abrupt motion tracking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it