Using a NIR Camera for Car Gesture Control
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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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".