LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a simple yet effective framework, named LightTrack, for online human pose tracking. Existing methods usually perform human detection, pose estimation and tracking in sequential stages, where pose tracking is regarded as an offline bipartite matching problem. Our proposed framework is designed to be generic, efficient and truly online for top-down approaches. For efficiency, Single-Person Pose Tracking (SPT) and Visual Object Tracking (VOT) are incorporated as a unified online functioning entity, easily implemented by a replaceable single-person pose estimator. To mitigate offline optimization costs, the framework also unifies SPT with online identity association and sheds first light upon bridging multiperson keypoint tracking with Multi-Target Object Tracking (MOT). Specifically, we propose a Siamese Graph Convolution Network (SGCN) for human pose matching as a Re-ID module. In contrary to other Re-ID modules, we use a graphical representation of human joints for matching. The skeleton-based representation effectively captures human pose similarity and is computationally inexpensive. It is robust to sudden camera shifts that introduce human drifting. The proposed framework is general enough to fit other pose estimators and candidate matching mechanisms. Extensive experiments show that our method outperforms other online methods and is very competitive with offline state-of-the-art methods while maintaining higher frame rates. Code and models are publicly available at https://github.com/Guanghan/lighttrack.
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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 it