Using a NIR Camera for Car Gesture Control
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
As digital components are increasingly present in the control of automotive engines, direction systems and other in-car devices, Human-Vehicle Interaction (HVI) becomes more and more complex, requiring new user interfaces. Gesture control is proposed in the literature as a techniques which deserves to be explored as it can tremendously simplify numerous interactions between the car and the driver and/or other passengers. Key characteristics of such HVI devices include reliability, robustness, and stability of the entire system, ranging from the acquisition of the gesture to its recognition and tracking in real-time. In this paper, a smart and real-time depth camera operating in the Near Infrared (NIR) Spectrum is introduced. The camera is based on a new depth generation principle of sampling the space of the Field-of-View (FOV) with IR pulses of variable frequency and duty cycle. The depth images are calculated using reconfigurable hardware architecture and a series of eight IR images obtained via a sensitive image sensor. The final depth map is then processed by the gesture detection, recognition and tracking algorithms. A series of gestures are explored to qualify them for the special case of car control.
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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 | 0.000 |
| 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