Towards a Robust Spatio-Temporal Interest Point Detection for Human Action Recognition
Bibliographic record
Abstract
Spatio-temporal salient features are widely being used for compact representation of objects and motions in video, especially for event and action recognition. The existing feature extraction methods have two main problems: First, they work in batch mode and mostly use Gaussian (linear) scale-space filtering for multi-scale feature extraction. This linear filtering causes the blurring of the edges and salient motions which should be preserved for robust feature extraction. Second, the environmental motion and ego disturbances (e.g., camera shake) are not usually differentiated. These problems result in the detection of false features no matter which saliency criteria is used. To address these problems, we developed a non-linear (scale-space) filtering approach which prevents both spatial and temporal dislocations. This model can provide a non-linear counterpart of the Laplacian of Gaussian to form the conceptual structure maps from which multi-scale spatio-temporal salient features are extracted. Preliminary evaluation shows promising result with false detection being removed.
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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 | 0.000 |
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".