Hands and Faces, Fast: Mono-Camera User Detection Robust Enough to Directly Control a UAV in Flight
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 0.003 |
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".