Bibliographic record
Abstract
Surveillance robots have extensive application potential in parks, gardens, plazas, malls, commercial centers, and many industrial sites, which have been relying on security officers for patrolling on foot. In order to succeed in these important applications, a surveillance robot needs to be able to move along predefined patrolling route autonomously and detect potential illegal trespassing or anomalies without continuous human intervention. This paper focuses on autonomous surveillance using low-cost cameras. A Raspberry Pi camera module was integrated with a modified commercial mobile robot TurtleBot III, which was used as the surveillance robot platform during the experiments. Furthermore, a modified YOLO (you only look once) algorithm was implemented for autonomous trespassing detection. Extensive experiments were conducted, and the experimental results confirmed that the surveillance robot could detect a person when she/he is within 15 meters even if a portion of her/his body was not in the field of view. It was also identified that the detection performance was affected by lighting conditions and complexity of the background. Furthermore, tuning the threshold of confidence score from 50% to 45% can effectively avoid missing detection during the experiments. The experimental results will definitely benefit the development of autonomous surveillance robots.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".