Sledování více osob ve videu z jedné kamery
Bibliographic record
Abstract
Detekcia a sledovanie viacerých osôb je náročný problém s vysokým aplikačným potenciálom. Náročnosť problému je spôsobená hlavne zložitosťou scény a vysokou variabilitou v artikulácii a vzhľade osôb. Cieľom tejto práce je navrhnúť a implementovať systém schopný detekcie a sledovania viacerých osôb vo videu z jednej statickej kamery. K tomuto účelu bola navrhnutá on-line metóda založená na princípe sledovania pomocou detekcie. Navrhnutá metóda kombinuje detekciu, sledovanie a združovanie odoziev k dosiahnutiu presných výsledkov. Implementácia bola vyhodnotená na dostupnom datasete a výsledky ukázali, že je použiteľná k tomuto účelu. K zlepšeniu výsledkov sledovania bola navrhnutá a implementovaná robustná metóda segmentácie pohybu. Okrem toho, implementácia detektora založeného na histograme orientovaných gradientov bola zrýchlená s využitím grafického procesoru (GPU).
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".