Active-vision-based multisensor surveillance - an implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a novel reconfigurable surveillance system that incorporates multiple active-vision sensors is presented. The proposed system has been developed for visual-servoing and other similar applications, such as tracking and state estimation, which require accurate and reliable target surveillance data. In the specific implementation case discussed herein, the position and orientation of a single target are surveyed at predetermined time instants along its unknown trajectory. Dispatching is used to select an optimal subset of dynamic sensors, to be used in a data-fusion process, and maneuver them in response to the motion of the object. The goal is to provide information of increased quality for the task at hand, while ensuring adequate response to future object maneuvers. Our experimental system is composed of a static overhead camera to predict the object's gross motion and four mobile cameras to provide surveillance of a feature on the object (i.e., target). Object motion was simulated by placing it on an xy table and preprogramming a path that is unknown to the surveillance system. The selected cameras are independently and optimally positioned to estimate the target's pose (a circular marker in our case) at the desired time instant. The target data obtained from the cameras, together with their own position and bearing, are fed to a fusion algorithm, where the final assessment of the target's pose is determined. Experiments have shown that the use of dynamic sensors, together with a dispatching algorithm, tangibly improves the performance of a surveillance system
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it