A Metaheuristic Bat-Inspired Algorithm for Full Body Human Pose Estimation
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the problem of full body articulated human motion tracking from multi-view video data recorded in a laboratory environment. The problem is formulated as a high dimensional (31-dimensional) non-linear optimization problem. In recent years, metaheuristics such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Immune System (AIS), Firefly Algorithm (FA) are applied to complex non-linear optimization problems. These population based evolutionary algorithms have diversified search capabilities and are computationally robust and efficient. One such recently proposed metaheuristic, Bat Algorithm (BA), is employed in this work for full human body pose estimation. The performance of BA is compared with Particle Filter (PF), Annealed Particle Filter (APF) and PSO using a standard data set. The qualitative and the quantitative evaluation of the performance of full body human tracking demonstrates that BA performs better then PF, APF and PSO.
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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 | 0.001 | 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 it