Multi-Modal Human Action Segmentation Using Skeletal Video Ensembles
Bibliographic record
Abstract
Beyond traditional surveillance applications, sensor-based human action recognition and segmentation responds to a growing demand in the health and safety sector. Recently, skeletal action recognition has largely been dominated by spatio-temporal graph convolutional neural networks (ST-GCN), while video-based action segmentation research successfully employs 3D convolutional neural networks (3D-CNNs), as well as vision transformers. In this paper, we argue that these two inputs are complementary, and we develop an approach that achieves superior performance with a multi-modal ensemble. A multi-task GCN is developed that can predict both frame-wise actions as well as action timestamps, allowing for the use of fine-tuned video classification models to classify action segments and achieve refined predictions. Symmetrically, a multi-task video approach is presented that uses a video action segmentation model to predict framewise labels and timestamps, augmented with a skeletal action classification model. Finally, an ensemble of segmentation methods for each modality (skeletal, RGB, depth, and infrared) is formulated. Experimental results yield 87% accuracy on the PKU-MMD v2 dataset, delivering state-of-the-art performance.
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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 | 0.001 |
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".