A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies
Bibliographic record
Abstract
Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine the distances between humans. The detection model uses bounding box information to identify persons. The identified bounding box centroid's pairwise distances of people are calculated using the Euclidean distance. Also, we used jetson nano platform to implement a low-cost embedded system and IoT techniques to send the images and notifications to the nearest police station to apply forfeit when it detects people’s congestion in a specific area. Lastly, the suggested system has the capability to assist decrease the intensity of the spread of COVID-19 and other diseases by identifying violated social distance measures and notifying the owner of the system. Using the transformation matrix and accurate pedestrian detection, the process of detecting social distances between individuals may be achieved great confidence. Experiments show that CNN-based object detectors with our suggested social distancing algorithm provide reasonable accuracy for monitoring social distancing in public places, as well.
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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 | 0.000 | 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 | 0.001 | 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 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".