New Object Tracker Based On Adaptive Intensity Models of Object and Its Surroundings
Bibliographic record
Abstract
New developments on the object tracker topic are needed, so that reliable tracking systems can have value for industrial applications, like surveillance and assembly monitoring.This paper presents a new object tracker algorithm based on adaptive models of the intensity probabilities of the object and its surroundings.Using the tracked object contour in the previous frame and the object path allow to estimate a narrow search area, in which contours with high object probability are combined, after masking pixels with high surrounding probabilities away.Rules about the object contour area ensures that the tracked contour doesn't drift away between frames or spreads into the surroundings.If the tracking is lost, the contour prediction in combination with the surrounding estimation takes over, filling the gaps until the object intensity-based tracker leads the tracking again.The proposed tracker was contrasted against three of the available trackers in OpenCV (i.e.KCF, CRST and MOSSE).Their performances were evaluated on two different applications (i.e.drone tracking and part tracking in an assembly cell) based on the Intersection over Union (IoU)-metric and their processing time.The obtained results show that the proposed tracker is faster and more accurate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".