A real-time system for high-level video representation: application to video surveillance
Bibliographic record
Abstract
The steadily increasing need for video content accessibility necessitates the development of stable systems to represent video sequences based on their high-level (semantic) content. The core of such systems is the automatic extraction of video content. In this paper, a computational layered framework to effectively extract multiple high-level features of a video shot is presented. The objective with this framework is to extract rich high-level video descriptions of real world scenes. In our framework, high-level descriptions are related to moving objects which are represented by their spatio-temporal low-level features. High-level features are represented by generic high-level object features such as events. To achieve higher applicability, descriptions are extracted independently of the video context. Our framework is based on four interacting video processing layers: enhancement to estimate and reduce noise, stabilization to compensate for global changes, analysis to extract meaningful objects, and interpretation to extract context-independent semantic features. The effectiveness and real-time response of the our framework are demonstrated by extensive experimentation on indoor and outdoor video shots in the presence of multi-object occlusion, noise, and artifacts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".