Multi-target surveillance in dynamic environments: sensing-system reconfiguration
Bibliographic record
Abstract
The active surveillance of maneuvering targets with multiple dynamic sensors requires a planning strategy to dynamically select and position the groups of sensors for optimal performance. This Thesis presents such a generic sensor-planning strategy that can be used for the real-time reconfiguration of a multi-sensor system for multi-object dynamic environments. The environment may include multiple Objects-of-Interest (OoI) as well as static and/or mobile objects that are not of interest (i.e., obstacles) but may act as occlusions. There should be no restrictions imposed on the number of sensors or their mobility as well as no requirement about knowing the objects' trajectories ahead of time. It is proposed herein to handle the sensor-planning problem using two complementary strategies: A coordination strategy to determine how many and specifically which sensors should be used at each demand instant in order to optimize the performance of the surveillance system over the span of several data-acquisition instants; and, a positioning strategy to determine the optimal pose of each sensor for any data-acquisition instant being serviced. In order to demonstrate the generality of the proposed methodology, it has been implemented in active-sensing for object localization, facial recognition, and object recognition via Shape-From-Shading (SFS). The applications cover surveillance of both time-variant and invariant parameters. Furthermore, they demonstrate the advantages of surveillance systems that utilizes multiple mobile sensors coupled with an effective sensor-planning strategy over static or single-sensor systems. The improvements in surveillance performance are primarily due to (i) increased robustness of the system (i.e., its ability to cope with a priori unknown target trajectories and presence of obstacles), (ii) decreased uncertainty associated with estimating the target's pose through sensor fusion, and (iii) increased reliability through sensory fault tolerance. The coordination strategy is proposed to be accomplished in this Thesis through an agent-based system consisting of multiple sensor agents, a referee agent, and a judge agent. Each sensor agent tries to maximize its own performance over the span of the rolling horizon. Although not directly controlled by a centralized controller, the sensor agents must abide the external rules of the environment monitored by the referee agent and enforced by the judge agent. The rules are set to ensure that the collective behaviour of the sensor agents exhibits the desired system behaviour. The positioning strategy is accomplished by determining regions of a sensor's workspace that are both unoccluded and achievable given the sensor's current pose, motion capabilities, and remaining time to data acquisition. The acceptable area is, then, searched for an optimal sensor pose.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".