AI-Powered Video Monitoring: Assessing the NVIDIA Jetson Orin Devices for Edge Computing Applications
Bibliographic record
Abstract
This paper evaluates the performance of the NVIDIA Jetson Orin family of devices for AI and edge computing applications, focusing on a parking lot surveillance example with CVEDIA-RT software. The NVIDIA Jetson Orin AGX Developer Kit is used as a means to emulate the Orin NX and Orin Nano devices. A testing procedure based on augmented scripts is presented to assess key performance indicators like RAM, GPU and CPU usage across the Orin NX, and Nano models. By employing the parking lot footage as a real-world test for intruder detection, it was found that all models consistently deliver at least an average of 10 FPS, with higher-end models outperforming the lower-end Orin Nano device. Additionally, the YOLOv4 algorithm is deployed with DeepStream on the Jetson Orin Nano Developer Kit, showcasing that the 15 W configuration is suitable for surveillance applications, achieving 13 average FPS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".