SKELETRACK: Efficient Tracking of Skeleton in Blurry Videos for Human Activity Recognition
Bibliographic record
Abstract
Human pose estimation is the task of predicting body joint locations and orientations of a person in an image or video. This problem has many practical applications in areas such as sports analysis, surveillance, and human activity recognition. However, current state-of-the-art approaches struggle with detecting fast-moving objects as they appear blurry in videos or images, which happens frequently in real-world scenarios. In this paper, we present the architecture of a top-down multi-person pose estimation framework to address the above problem, specifically, human recognition from blurry videos. Our pipeline can track the spatio-temporal information in the input videos to detect and track human skeletons where object tracking algorithms fail and create an estimated bounding box based on the moving target object’s velocity and direction. We validate our algorithm using the FineGym dataset containing fast moving athletes performing gymnastics for which the current state-of-the-art accuracy in human activity recognition is ${2 5. 2 \%}$. Skeletrack achieves 66.55% Top-1 and 89.36% Top-5 mean accuracy in skeleton based activity recognition on FineGym.
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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 | 0.000 |
| 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".