A victim identification methodology for rescue robots operating in cluttered USAR environments
Why this work is in the frame
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Bibliographic record
Abstract
Our research focuses on developing an automated victim identification methodology for rescue robots in order to aid robot operators with the complex and stressful task of searching for victims in cluttered urban search and rescue (USAR) environments. In this paper, we present an approach that utilizes 2D and 3D sensory information from a real-time 3D sensory system for robust victim identification using both human geometric and skin region features. Our technique, uniquely, allows for the identification of partially occluded victims and single body parts that may be visible in cluttered USAR scenes using a Support Vector Machine-based classifier based on the aforementioned features. Unlike other approaches that focus on the recognition of one specific body part (such as the head) or the recognition of a small set of fixed body poses, we aim to identify multiple different body parts in a number of varying configurations to increase recognition rate. Experimental results illustrate the robustness of our methodology to find human victims in a variety of different poses in a rubble-filled USAR-like scene and its ability to potentially reduce operator workload.
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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