3D Human Activity Classification with 3D Zernike Moment Based Convolutional, LSTM-Deep Neural Networks
Bibliographic record
Abstract
In this paper, we propose a method for classification 3D human activities using the complementarity of CNNs, LSTMs, and DNNs by combining them into one unified architecture called CLDNN. Our approach is based on the prediction of 3D Zernike Moments of some relevant joints of the human body through Kinect using the Kinect Activity Recognition Dataset. KARD includes 18 activities and each activity consists of real-world point clouds that have been carried out 3 times by 10 different subjects. We introduce the potential for the 3D Zernike Moment feature extraction approach via a 3D point cloud for human activity classification, and the ability to be trained and generalized independently from datasets using the Deep Learning methods. The experimental results obtained on datasets with the proposed system has correctly classified 96.1% of the activities. CLDNN has been shown to provide a 5% relative improvement over LSTM, the strongest of the three individual models.
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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) | 0.001 | 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".