Unsupervised camera network structure estimation based on activity
Bibliographic record
Abstract
In this paper we consider the problem of unsupervised topology reconstruction in uncalibrated visual sensor networks. We assume that a number of video cameras observe a common scene from arbitrary and unknown locations, orientations and zoom levels, and show that the extrinsic and calibration matrices, fundamental and essential matrices, the homography matrix, and the physical configuration of the cameras with respect to each other can be estimated in an unsupervised manner. Our method relies on the similarity of activity patterns observed at various locations, and an unsupervised matching method based on these activity patterns. The proposed method works in cases with cameras having significantly different orientations and zoom levels, where many of the existing methods cannot be applied. We explain how to extend the method to a multicamera case where more than two cameras are involved. We present both qualitative and quantitative results of our estimates, and conclude that this method can be applied in wide area surveillance applications in which the deployed systems need to be flexible and scalable, and where calibration can be a major challenge.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".