INTEGRATION OF VIDEO IMAGES AND CAD WIREFRAMES FOR 3D OBJECT LOCALIZATION
Bibliographic record
Abstract
Abstract. The tracking of moving objects from single images has received widespread attention in photogrammetric computer vision and considered to be at a state of maturity. This paper presents a model-driven solution for localizing moving objects detected from monocular, rotating and zooming video images in a 3D reference frame. To realize such a system, the recovery of 2D to 3D projection parameters is essential. Automatic estimation of these parameters is critical, particularly for pan-tilt-zoom (PTZ) surveillance cameras where parameters change spontaneously upon camera motion. In this work, an algorithm for automated parameter retrieval is proposed. This is achieved by matching linear features between incoming images from video sequences and simple geometric 3D CAD wireframe models of man-made structures. The feature matching schema uses a hypothesis-verify optimization framework referred to as LR-RANSAC. This novel method improves the computational efficiency of the matching process in comparison to the standard RANSAC robust estimator. To demonstrate the applicability and performance of the method, experiments have been performed on indoor and outdoor image sequences under varying conditions with lighting changes and occlusions. Reliability of the matching algorithm has been analyzed by comparing the automatically determined camera parameters with ground truth (GT). Dependability of the retrieved parameters for 3D localization has also been assessed by comparing the difference between 3D positions of moving image objects estimated using the LR-RANSAC-derived parameters and those computed using GT parameters.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".