Extremely Low-resolution RFID Vision for Real-time and Visually-anonymized Action Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Despite the potential of vision-based personal monitoring (e.g., healthcare), private data leakage concerns hinder its wide deployment in personal spaces (e.g., bedrooms). A body of data anonymization designs was proposed throughout image processing and federated learning. They commonly store high-quality images and videos locally, which are anonymized via post-processing before cloud upload. However, the recent IoT camera hacking and local data leakage call for anonymized data at the sensing stage. Also, continuous and pervasive monitoring without blind spots in complicated indoor spaces requires a scalable and economic system. This article presents Mosaic , a vision-based end-to-end action recognition framework that (i) intrinsically achieves data anonymity from the sensing stage and (ii) battery-free operation for blind spot-free continuous monitoring. Mosaic leverages an extremely low resolution (eLR) Near-Infrared (NIR) image sensor with 6 \(\times\) 10 pixels for video anonymity and an RFID-compliant fully-passive tag with four solar cells for real-time eLR video streaming under as low as 30 lux (e.g., deep in the shelf without direct light). This is accompanied by a lightweight action recognition neural network for real-time inference (18.4 ms on Intel(R) Core i7-8700). Mosaic achieves an average of 98% accuracy on 10 action classes, hitting the balance between data anonymity and high-precision action recognition. Taking advantage of the NIR (non-visible) frequency, Mosaic also works in the dark without disturbing sleep. Lastly, wildfire detection reaching 20 m was demonstrated, showcasing the potential for outdoor monitoring.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it