Chaotic Synchronization of MPEG-7 Descriptors for Interpretation in Surveillance Video
Bibliographic record
Abstract
The research in video surveillance is moving towards semantic (i.e., meaning) analysis of the scene contents through high-level descriptions. Chaos theory has been reported to simulate partial functions (i.e., neuronal activity in brain) of the human visual system. In this work, we propose a chaotic synchronization-based method to identify semantic entities in surveillance scenes. MPEG-7 visual descriptors (Ds) are used to extract low-level features of video objects. These objects are generated per video frame by segmentation and tracking. The chaotic synchronization is used to perform feature binding (i.e., group semantically relevant feature elements) from these Ds. The objective is to search for unique numeric descriptions (based on low-level features) to identify semantic entities. The idea of a semantic space is introduced to explain feature binding from multiple features spaces. Subjective result evaluation of our results show the existence of such numeric description for related semantic entities (e.g., male, female, enter, deposit, take object, two person meet)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 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".