Infrared target tracking based on multi-feature correlation filter
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
In order to realize robust tracking of infrared target in complicated background with lots of disturbed factors,this paper proposes an infrared target tracking method based on multi-feature correlation filter.Considering the visual attention mechanism and motion mechanism,the spatial feature and motion feature are extracted firstly.Then the multi-feature weighted function is generated by fusing the above two features and the improved convolution feature.Secondly,on the basis of traditional correlation filter,the tracking framework vie weighted correlation filter is presented by introducing multi-feature weighted function which could represent the importances of different candidate regions.Finally,the confidence map which indicates the best target location is computed.The experiments under 6sequences with different conditions demonstrate that the average increase of success rate of the proposed method has increased by about 15%compared with other traditional methods,and the proposed method is applicable to infrared target tracking under different conditions efficiently,such as similar alias target,occlusion,thermal radiance variation of background and detector motion.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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