Towards Efficient Video Stream Analysis: A Distributed Deep Learning Framework: The DiVA Approach
Bibliographic record
Abstract
The advent of advanced computational devices and Neural Networks (NN) has triggered a paradigm shift in object detection, a key area of Artificial Intelligence (AI).This progress has significantly improved the accuracy of object identification in images, demonstrating the transformative power of deep learning.However, real-time video stream processing with deep learning models remains a challenge.This paper presents Distributed Video Analytics (DiVA), a scalable platform designed to address these issues using deep learning and event processing for real-time video analysis.It explores quantification techniques, optimization tools, and a high-level conceptual architecture to enhance video stream analysis.The study includes experiments evaluating the You Only Look Once version 8 small (YOLOv8s) model across various frameworks, hardware configurations, and optimization strategies.The results show substantial performance gains, particularly with Graphics Processing Unit (GPU) processing and advanced frameworks like NVIDIA Triton Server and Deepstream SDK, optimized with NVIDIA TensorRT and INT8 quantization.The findings highlight DiVA's effectiveness in improving performance, energy efficiency, and scalability for deep learning inference and model deployment.Notably, the best configuration achieved 47.2 frames per second (FPS), showcasing significant processing efficiency.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".