Scale and View Invariant Informative Joint Descriptor (SVI2JD) for Human Action Recognition from Skeleton Data
Bibliographic record
Abstract
One of the biggest challenges in the Human Action Recognition is View-point variations as the actions are captured under multiple views in real time.Furthermore, in the Skelton based action representation, for HAR, only few joints are informative and remaining joints constitutes redundancy.To sort out these problems, this paper proposes a new Action descriptor called as Scale and View Invariant Informative Joint Descriptor (SVI 2 JD).SVI 2 JD is a combination of three descriptors; they are namely Self-Similarity Joint Descriptor (SSJD), Informative Joint Descriptor (IJD) and Spherical Joint Descriptor (SJD).SSJD concentrates on the view invariance and employs a Self-Similarity Matrix (S 3 M) which computes pair wise distance between joints in each frame of action sequence.Next, SJD aims at describing the action through restricted movements of joints because they can't move beyond particular angle and distance from origin of body.IJD removes the redundant joints those have less contribution towards the action.Further, a 2D Convolution Neural Network Model is proposed for feature extraction and classification.Different fusion rules are employed to fuse the individual results.The Effectiveness of proposed model is demonstrated through its simulation on two challenging datasets; NTU RGB+D dataset and Northwestern UCLA dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".