Demo: A distributed virtual vision simulator
Bibliographic record
Abstract
Realistic virtual worlds can serve as laboratories for carrying out camera networks research. This unorthodox “Virtual Vision” paradigm advocates developing visually and behaviorally realistic 3D environments to serve the needs of computer vision. Our work on high-level coordination and control in camera networks is a testament to the suitability of virtual vision paradigm for camera networks research. The prerequisite for carrying out virtual vision research is a virtual vision simulator capable of generating synthetic imagery from simulated real-life scenes. We present a distributed, customizable virtual vision simulator capable of simulating pedestrian traffic in a variety of 3D environments. Virtual cameras deployed in this synthetic environment generate synthetic imagery - boasting realistic lighting effects, shadows, etc. - using the state-of-the-art computer graphics techniques. The synthetic imagery is fed into a “real-world” vision pipeline that performs visual analysis - e.g., blob detection and tracking, facial detection, etc. - and returns the results of this analysis to our simulated cameras for subsequent higher level processing. It is important to bear in mind that our vision pipeline is designed to handle real world imagery without any modifications. Consequently, it closely mimics the performance of a vision pipeline that one might deploy on physical cameras. Our virtual vision simulator is realized as a collection of modules that communicate with each other over the network. Consequently, we can deploy our simulator over a network of computers, allowing us to simulate much larger networks and much more complex scenes then is otherwise possible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".