Joint-Triplet Motion Image and Local Binary Pattern for 3D Action Recognition Using Kinect
Bibliographic record
Abstract
This paper presents a new action recognition method that utilizes the 3D skeletal motion data captured using the Kinect depth sensor. We propose a robust view-invariant joint motion representation based on the spatio-temporal changes in relative angles among the different skeletal joint-triplets, namely the joint relative angle (JRA). A sequence of JRAs obtained for a particular joint-triplet intuitively represents the level of involvement of those joints in performing a specific action. Collection of all joint-triplet JRA sequences is then utilized to construct a spatial holistic description of action-specific motion patterns, namely the 2D joint-triplet motion image. The proposed method exploits a local texture analysis method, the local binary pattern (LBP), to highlight micro-level texture details in the motion images. This process isolates prototypical features for different actions. LBP histogram features are then projected into a discriminant Fisher-space, resulting in more compact and disjoint feature clusters representing individual actions. The performance of the proposed method is evaluated using two publicly available Kinect action databases. Extensive experiments show advantage of the proposed joint-triplet motion image and LBP-based action recognition approach over existing methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".