Bibliographic record
Abstract
Preserving the privacy of people in video surveillance systems is quite challenging, and a significant amount of research has been done to solve this problem in recent times. Majority of existing techniques are based on detecting bodily cues such as face and/or silhouette and obscuring them so that people in the videos cannot be identified. We observe that merely hiding bodily cues is not enough for protecting identities of the individuals in the videos. An adversary, who has prior contextual knowledge about the surveilled area, can identify people in the video by exploiting the implicit inference channels such as behavior, place, and time. This article presents an anonymous surveillance system, called Watch Me from Distance (WMD), which advocates for outsourcing of surveillance video monitoring (similar to call centers) to the long-distance sites where professional security operators watch the video and alert the local site when any suspicious or abnormal event takes place. We find that long-distance monitoring helps in decoupling the contextual knowledge of security operators. Since security operators at the remote site could turn into adversaries, a trust computation model to determine the credibility of the operators is presented as an integral part of the proposed system. The feasibility study and experiments suggest that the proposed system provides more robust measures of privacy yet maintains surveillance effectiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".