Moving objects tracking from most probable regions and eliminating camera motion
Bibliographic record
Abstract
This paper presents a novel method for moving object tracking in different scales. There are researches in tracking objects but most of them focus on specific subject and fail in some conditions such as changing position, moving camera, changing scale because of the distance variations. Camera movement is one of the most challenging events which causes to have a lot of fake moving objects in scenes. In this paper we modify KLT (Kanade- Lucas- Tomasi) algorithm by spectral residual in different Gaussian pyramid scales and extract positions with high probability of objects presence. To achieve perfect tracking, consecutive frames are rectified by finding the best matches between features points and remove undesired effects of camera movements. To evaluate the proposed approach, we arrange experiments using standard databases and compare with the other methods reported in the literature. The results indicate that the proposed approach is capable of detecting and tracking all the moving objects in acceptable accuracy rate, i.e., over 90% accuracy in average in all challenging databases.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".