Semantic Video Compression on Embedded Devices for Satellite-Assisted Remote Surveillance
Bibliographic record
Abstract
This paper presents a semantic video compression framework designed for remote surveillance in bandwidth-constrained environments, including satellite communications. Our proposed system represents a semantic communication approach to extract and transmit only meaningful visual information, focusing on objects of interest and their associated attributes, while discarding redundant background data. Using YOLOv8 object detection and multi-object tracking, we propose four levels of semantic representations, ranging from basic object attributes to detailed annotated frames. Initially validated on high-performance hardware, we adapt the approach for resourceconstrained platforms such as the Raspberry Pi 4 Model B, with acceleration provided by the Coral USB TPU Accelerator. Our experimental results demonstrate near real-time performance with delays of approximately 1.5 seconds, and significant retained data ratio ranging between 0.16% and 3.25%, suitable for lowbandwidth remote surveillance on edge devices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".