Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences
Bibliographic record
Abstract
Human action recognition based on depth action sequences is a well-known study that can be used in many fields.Compared to RGB Videos, depth action videos are more robust as they are not affected by changes in lighting.This study proposes the Enhanced Depth Motion Map (EDMM), a new action descriptor to overcome the challenges of the conventional DMM, which cannot handle the presence of some undefined regions in depth maps.Contrary to DMM's global motion description, the EDMM meticulously scans individual pixels and then accurately identifies those in motion.We extracted the EDMM from a series of video sequences and then used a convolutional neural network (CNN) model to simplify the motions accurately.The CNN model, equipped with nine layers, accurately recognizes activities based on maximum movement similarity.The method underwent testing using two standard and publicly available datasets; MSR Action 3D and UTD-MHAD.The test results through True Positive Rate (TPR), Positive Predictive Value (PPV) or Precision, False Discovery Rate (FDR), False Negative Rate (FNR), F1-score, and accuracy demonstrated the superiority of the proposed method over numerous state-of-the-art methods like DMM, DMM with Local Binary Pattern and DMM with Histogram of Oriented Gradients (HOGs).
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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 | 0.001 | 0.002 |
| 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".