Surveillance Video Representations for Bandwidth-limited Semantic Communications
Why this work is in the frame
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Bibliographic record
Abstract
Video data, particularly from surveillance cameras, significantly contribute to network traffic. In emerging communication networks, the semantic communication paradigm addresses this issue by enabling task-oriented transmission to reduce data sizes. In this context, we propose a semantic video compression framework that generates abstract representations of relevant information in surveillance videos. The semantic encoder includes an object detection and tracking component that extracts contextual information about objects of interest. A video language model component provides optional descriptive captions of the video. The extracted information is incorporated in compact semantic representations generated according to bandwidth constraints. We introduce the semantic information index (SII) metric that evaluates how well the framework preserves the videos’ meaning in relation to surveillance tasks. The framework achieves a retained data ratio (RDR) ranging between 1% and 12% of the initial video size for various SII values.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it