Gesture Detection Using an Infrared Camera
Bibliographic record
Abstract
Gestures and body language exert a significant impact on communication and interactions among individuals. This project's objective entailed the development of a gesture recognition system that analyzes the way people interact with their surroundings. The careful selection of the five recognized gestures ensures that the system remains focused on key limb placements that may dictate how one is perceived by the surrounding public. As the infrared (IR) camera captures real-time data, the system's ability to operate in low-light conditions further adds to its practicality and versatility in various environments. The convolutional neural network (CNN) plays a central role in the system's accuracy and efficiency. Its intensive training on a diverse dataset of images allows it to discern the distinct visual patterns associated with each gesture. As a result, the algorithm's Mean Average Precision (mAP) of 71.84% attests to its proficiency in accurately recognizing and classifying gestures.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".