Hands and Faces, Fast: Mono-Camera User Detection Robust Enough to Directly Control a UAV in Flight
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a robust real-time system for simultaneous detection of hands and faces in RGB and gray-scale images, and a novel dataset used for training. Our goal is to provide a robust sensor front-end suitable for real-time human-robot interaction using face-engagement and gestures. Using hand-labelled videos obtained from real human-UAV interaction experiments, we re-trained the YOLOv2 Deep Convolutional Neural Network to detect only hands and faces. This model was then used to automatically label several much larger third-party datasets. After manual correction of these results, we modified and re-trained the model on all this labelled data. We obtain qualitatively good detection results at 60Hz on a commodity GPU: our simultaneous hand-and-face detector gives state of the art accuracy and speed in a hand detection benchmark and competitive results in a face detection benchmark. To demonstrate its effectiveness for human-robot interaction we describe its use as the input to a simple but practical gestural human-UAV interface for entertainment or industrial applications. All software, training and test data are freely available.
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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 | 0.000 |
| 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