Efficient Parameter Based Online Object Tracking
Bibliographic record
Abstract
Traditional object tracking approaches solve the problem as a supervised learning task. Due to variations in video frames over time, the appearance model of the object is updated for each video frame. Using density or mixture based model to represent target object is a common technique in Bayesian approaches. However, in histogram based representation where the data is proportional, Gaussian distribution is not the proper choice. In this paper, we propose a framework for feature representation on the simplex manifold for proportional data utilizing the histogram representation of the target object in initial frame. Extracted density features are concatenated with histogram based features to get a better representation of the target object. A set of parameter vectors determine the appearance features of the target object in the subsequent frames. To precisely model proportional data, we employ five appropriate distributions; namely, Dirichlet, Generalized Dirichlet, Scaled Dirichlet, Beta-Liouville and Inverted Dirichlet distributions. We present experimental results to show that the proposed method, which incorporates both generative and discriminative features, has an improved tracking performance.
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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 | 0.002 |
| 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) | 0.002 | 0.002 |
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".