Active-Vision for the Autonomous Surveillance of Dynamic, Multi-Object Environments
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel method for the coordinated selection and positioning of groups of active-vision cameras for the autonomous surveillance of an object-of-interest as it travels through a multi-object workspace with an a priori unknown trajectory. Several approaches have been previously proposed to address the problem of sensor selection and control. However, these have primarily relied on off-line planning methods and only infrequently utilized on-line planning to compensate for unexpected variations in a target’s trajectory. The method proposed in this paper, on the other hand, uses a real-time dispatching algorithm, which eliminates the need for any a priori knowledge of the target’s trajectory and, thus, is robust to unexpected variations in the environment. Experiments have shown that the use of dynamic sensors along with a dispatching algorithm can tangibly improve the performance of an active-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