Bibliographic record
Abstract
Network video cameras, invented in the last decade or so, permit today pervasive, wide-area visual surveillance. However, due to the vast amounts of visual data that such cameras produce human-operator monitoring is not possible and automatic algorithms are needed. One monitoring task of particular interest is the detection of suspicious behavior, i.e., identification of individuals or objects whose behavior differs from behavior usually observed. Many methods based on object path analysis have been developed to date (motion detection followed by tracking and inferencing) but they are sensitive to motion detection and tracking errors and are also computationally complex. We propose a new surveillance method capable of abnormal behavior detection without explicit estimation of object paths. Our method is based on a simple model of video dynamics. We propose one practical implementation of this general model via temporal aggregation of motion detection labels. Our method requires little processing power and memory, is robust to motion segmentation errors, and general enough to monitor humans, cars or any other moving objects in uncluttered as well as highly-cluttered scenes. Furthermore, on account of its simplicity, our method can provide performance guarantees. It is also robust in harsh environments (jittery cameras, rain/snow/fog).
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".