UNCLASSIFIED UNCLASSIFIED Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study
Bibliographic record
Abstract
Currently deployed video surveillance systems and protocols are not fully efficient. In real-time monitoring mode, the problem is that an event may easily pass unnoticed due to false or simultaneous alarms and lack of time needed to rewind and analyse all potentially useful video streams. In archival mode, video data storage and manageability is the problem that makes post-incident investigation very difficult.- Due to the temporal nature of video data, it is very difficult for a human to analyse video data within a limited amount of time. This paper presents an automated video surveillance technology named ACE Surveillance (Annotated Critical Evidence) that is developed by the National Research Council of Canada (NRC) for the purpose of enabling more efficient use of surveillance systems. This technology, which incorporates recent advances in objects detection and tracking, has been tested on several real-life long-term monitoring assignments, including an over a year testing with the existing CCTV surveillance cameras at the NRC campus. The results of these tests are described. – While quantitatively showing the advantage of using automated evidence extraction systems for enhanced security and providing a reference standard for measuring Intelligent Video systems available on the market, the presented study also exposes several problems related to the development and deployment of such systems. Further steps for integrating automated evidence extraction systems for mainstream security applications are discussed. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".