Detecção de Mudança para Identificação de Objetos Móveis em VÃdeo UHD
Bibliographic record
Abstract
Several sensors provide a very large range of data with different characteristics. In this line, this work aims to demonstrate an application for Data Fusion. An algorithm in OpenCV designed to detect moving objects in Ultra HD video purchased by Iris camera, installed on the Zvezda module of the International Space Station (ISS). The study area is located in Vancouver, Canada. The images are from Deimos-2 satellite in 1C level. The first stage of the research was to detect moving objects between successive frames of high resolution video. The detection of moving objects resulted in new images containing motion information, thus it was possible to separate the moving objects in the scene. In order to highlight the changes were applied one thresholding in the change detection image creating a binary image. So as to reduce the possibility of false positives, especially those generated by the movement of the sensor, was applied a morphological operation erosion. Finally, a new video was created showing the moving objects over time, allowing monitoring of these objects. In the second stage, there was the match between the georeferenced panchromatic image and the video frames of moving object, which allowed the identification of the object position associated with the each frame of video to monitor the displacement of objects.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".