Infrared and 3D Skeleton Feature Fusion for RGB-D Action Recognition
Why this work is in the frame
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Bibliographic record
Abstract
For skeleton-based action recognition from depth cameras, distinguishing object-related actions with similar motions is a difficult task. The other available video streams (RGB, infrared, depth) may provide additional clues, given an appropriate feature fusion strategy. We propose a modular network combining skeleton and infrared data. A pre-trained 2D convolutional neural network (CNN) is used as a pose module to extract features from skeleton data. A pre-trained 3D CNN is used as an infrared module to extract visual features from videos. Both feature vectors are then fused and exploited jointly using a multilayer perceptron (MLP). The 2D skeleton coordinates are used to crop a region of interest around the subjects for the infrared videos. Infrared is favored over RGB, as it is less affected by illumination conditions and usable in the dark. We are the first to combine infrared and skeleton data. We evaluate our method on the NTU RGB+D dataset, the largest dataset for human action recognition from depth cameras. We perform extensive ablation studies. In particular, we show the strong contributions of our cropping strategy and pre-training on action classification accuracy. We also test various feature fusion schemes. Feature sum on an element-wise level yields the best results. Our method achieves state-of-the-art performances on NTU RBG+D.
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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 | 0.002 |
| 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 it