Edge-based Video Analytic for Smart Cities
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
Video analytic is the important tool for smart city development. The video analytic application requires more memories and high processing devices. The problems of cloud-based approach for video analytic are high latency and more network bandwidth to transfer data into the cloud. To overcome these problems, we propose a model based on dividing the jobs into smaller sub-tasks with less processing requirements in a typical video analytics application for the development of smart city. The object detection, tracking and pattern recognition method to reduce the size of videos based on edge network will be proposed. We will design a video analytic model, and simulation is performed using iFogSim simulator. We will also propose Convolutional Neural Network (CNN) based object tracking model. The experimental verification shows that our tracking model is more than 96% accurate, and the proposed edge and cloud-based model is more than 80% effective than only cloud-based approach for video analytic applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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