Visual Target Tracking using Robust Information Interaction between Single Tracker and Online Model
Bibliographic record
Abstract
In this paper, a novel tracking algorithm based on the cooperative operation of online appearance model and typical tracking in contiguous frames is proposed. First of all, to achieve satisfactory performances in challenging scenes, we focus on establishing a robust discriminative tracking model with linear Support Vector Machine (SVM) and use the particle filter for localization. Intended to fit the particle filter, the outputs of SVM classifier are mapped into probabilities with a sigmoid function so that the posterior of candidate samples is estimated. Then, the tracking loop starts with median flow method and the coordinated operation of the two trackers is mediated by the maximum a posteriori (MAP) estimate for the target probability of negative samples, which is defined during the sigmoid fit. Lastly, for the purpose of model update, we sum up the optimal SVM using a prototype set with the predefined budget, and the classifier is updated on both the prototype set and the updated data from the tracking results every few frames. A number of comparative experiments are conducted on real video sequences and both qualitative and quantitative evaluations demonstrate a robust and precise performance of our method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".