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
Video image has the advantage of large amount of information, good real-time performance and low cost, so automatic people counting based on video image has very high practical value, and many scholars have done a large number of experiments and studies on this and achieved certain achievements. But for scenes with more occlusion and background changing quickly and without obvious rules, it’s difficult to count accurately. In order to improve the counting accuracy in the above scenes, to provide the number of customers for hotel managers to efficiently organize and work, based on pictures, a automatic people counting method using SVM as weak classifiers, train intensively in learning by Adaboost algorithm(i.e. Adab_SVM algorithm) of hotel dining is proposed. The method is mainly aimed at the hotel scenes with occlusion too much to complete the segmentation of human body region. Firstly, traversing the entire picture to get the preliminary head areas and the number of people, then merge these head areas to get the exact number of people, to complete the statistical work on the number of people of the entire picture. Experimental results show that the method has higher counting accuracy in the hotel scenes with occlusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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