Human Tracking Using Spatialized Multi-level Histogram and Mean Shift
Bibliographic record
Abstract
Sequential object tracking using mean shift method has become a convenient approach. In this method, an object of interest is represented by its global feature such as a color histogram. The next position of the target is then estimated through a constraint histogram matching. The linearization of the histogram matching metric might not work properly, especially when the target undergoes occlusion, there is an abrupt motion, or when multiple objects exist with similar global but different local structures. We propose a multi-level global-to-local histogramming approach in which the associated spatial information is also encoded in the object's representation. Specifically, for human shape/appearance encoding, the global histogram resembles the main root and the local histograms correspond to the body parts. In an experiment on a publically available CAVIAR dataset, the proposed representation provides an appropriate sequential matching of a human with abrupt motion and partial occlusion. In addition to a better localization, the proposed approach handles the situations in which the standard mean shift fails.
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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 | 0.000 |
| 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".