Developing A Smart Home Surveillance System Using Autonomous Drones
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
Placing a number of home surveillance cameras around the property can enhance home security. However, camera coverage and their true effectiveness can be limited due to the limited number of cameras that can be installed, camera's field of view, camera's fixed position, and associated privacy issues. Unmanned aerial vehicles (UAVs), commonly known as drones, are able to fly independently without any human intervention. There are already a few commercially available options for outdoor drone surveillance, but none for indoor applications. We believe the drones can be effectively deployed for home monitoring purposes in a cost-effective and privacy-preserving manner. In this paper, we developed a novel autonomous drone prototype that can offer economically viable effective smart home monitoring capabilities than currently available home monitoring solutions in today's smart home industry. While in flight, our developed drone navigation system can fly on any predefined paths, dynamically change the paths based on user requirements to inspect any place within its range and adapt to unanticipated situations, such as obstacle avoidance and low battery. In addition, the system can utilize machine learning to evaluate the camera stream from the onboard camera and perform object detection tasks and notify users accordingly. In our testing, we demonstrated that our developed prototype successfully performed all the functions mentioned above. Also, our novel findings from this paper shed light on some of the important parameters of indoor drone-based monitoring systems, which will contribute to the further advancement in drone-based home monitoring technology,
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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