Classification of surveillance video objects using chaotic series
Bibliographic record
Abstract
The authors propose a framework for binary classification of challenging objects (e.g. incomplete, partial occluded, background over-lapped, scaled, outdoor) in surveillance video. The framework uses feature binding of MPEG-7 visual descriptors via chaotic series simulation. Diverse video objects are tested in multiple binary classifiers for generic classes (e.g. has_person, has_group_of_persons, has_vehicle and has_unknown). Object classification accuracy is verified with both low- and high-dimensional chaotic series-based feature binding. With high-dimensional chaotic series simulation: (i) the classification accuracy significantly improves on average, 83% compared with the 62% with the original MPEG -7 visual descriptors; (ii) ‘vehicle’ objects are clustered well, which leads to above 99% accuracy for only vehicles against other objects; and (iii) drifts in high-dimensional chaotic series, because of transient, allow the training feature vector to include subtle variations in MPEG-7 descriptor coefficients for video objects in a class. A higher variance in training feature vector, using high-dimensional chaotic series simulation, manifests these subtle variations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".