Automated video surveillance for preventing suicide attempts
Bibliographic record
Abstract
Inmate suicide by hanging is documented as a major cause of death in prisons. Important efforts have been made to develop technological prevention tools, but the proposed solutions are mostly using cumbersome devices, in addition to their lack of generalizability. Nowadays, computer vision methods for real-time video analysis have experienced impressive progress. The recent emergence of RGB-D cameras clearly illustrates the achieved advances by offering new ways for machines to interpret human activity. There was however no significant works on exploiting this evolution, and as a result, CCTV systems used for monitoring suicidal inmates are still greatly depending on human attention and intervention. This paper proposes an intelligent video surveillance system for automated detection of suicide attempts by hanging. The proposed algorithm is able to efficiently model suicidal behavior by exploiting the depth information captured by an RGB-D camera. Activity detection is then performed by classifying visual features characterizing body joint movements. Our method demonstrated a high robustness on a challenging dataset including video sequences where suicide attempts are simulated.
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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 | 0.000 | 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 | 0.000 | 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 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".