A Co-inference Approach to Robust Visual Tracking
Bibliographic record
Abstract
In Proc. of IEEE Int'l Conf. on Computer Vision, Vancouver, Canada, 2001 Visual tracking could be treated as a parameter estimation problem of target representation based on observations in image sequences. A richer target representation would incur better chances of successful tracking in cluttered and dynamic environments. However, the dimensionality of target's state space also increases making tracking a formidable estimation problem. In this paper, the problem of tracking and integrating multiple cues is formulated in a probabilistic framework and represented by a factorized graphical model. Structured variational analysis of such graphical model factorizes di#erent modalities and suggests a co-inference process among these modalities. A sequential Monte Carlo algorithm is proposed to give an e#cient approximation of the co-inference based on the importance sampling technique. This algorithm is implemented in real-time at around 30Hz. Specifically, tracking both position, shape and color distribution of a target is investigated in this paper. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios. The approach presented in this paper has the potential to solve other sensor fusion problems. 1
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".