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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".