A decision support engine for video surveillance systems
Bibliographic record
Abstract
Design and implementation of an automated or a semi automated surveillance system is an active research area. Safety concerns of individuals have accelerated the research for identifying alarming human activities in public spaces like streets, shopping malls, airports, and others. But reliance on human operator for real-time actions can be inappropriate and expensive. On the other hand, demonstrating pragmatic scene of a surveillanced area with the help of a synthetic world can be effective that can guard operator's limitation. In this paper, we design a Decision Support Engine (DSE) coupled with a synthetic space to facilitate surveillance activities of operators. For this purpose, the detailed sensory data are processed and alarms are detected, classified and ranked according to the threat severity that follows some well-defined rules of the system. At the end, the identified alarming cases are marked and presented with the help of a synthetic environment in an elegant way so that the operators can take the right action in a specific security circumstance.
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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 | 0.002 | 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 | 0.001 | 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".