Research on real – time tracking of table tennis ball based on machine learning with low-speed camera
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a novel method to track table tennis ball in real time by a low-speed camera instead of a high-speed one. Several difficult problems are solved for practical applications, such as environmental interference, smear in low-speed video images and slow processing speed. In view of these difficulties, the VOCUS system is used to segment images and mark the three significant regions based on three contrast colour channels. These regions are utilized for image matching using the LGP+adaboost algorithm. As a strong classifier based on machine learning, adaboost algorithm can recognize the features of smear balls with different shapes. Therefore, the region that is most similar to smear ball from the three significant regions is regarded as a target. Afterwards, through the moving ROI area algorithm, the identification time is greatly shortened in real-time video tracking. Finally, the feasibility of the algorithm is examined by experiments.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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