Design of a Computer Vision System for Human Pose Tracking Within the Aircraft Cabin
Bibliographic record
Abstract
Passenger comfort is a deterministic factor in today’s competitive air travel industry and particularly for business jets which are increasingly being designed and adapted to maximize comfort for their occupants. Sitting posture could be used as an effective way of gaining insight into passenger comfort. Despite which very few studies have focused on tracking sitting posture in aircraft passengers. Given recent advancements in Computer Vision (CV) and Deep Learning (DL), open-source pre-trained 3D Human Pose Estimation (HPE) algorithms are readily available and can be used to estimate human pose in various conditions. Thus, the primary aim of the thesis was to explore the application of the latest vision algorithms for passenger pose estimation in aircraft cabins and design a prototype for real-time seating pose detection. This entailed use of a pre-trained, open-source, 3D Human Pose Estimation algorithm, MediaPipe Pose, which detects humans in an RGB image, localizes their joint locations and outputs 3D pose landmarks (keypoint locations) in real-time using MediaPipe framework and OpenCV library. This prototype was tested in a real aircraft cabin and the results from the pose estimation were used to draw 3D animation to confirm real-time pose tracking. The system was able to estimate body poses regardless of sitting posture (active, passive, lateral bending to left/right). As such, this thesis proposes a prototype for real-time vision-based 3D pose tracking in aircraft passengers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".