A Parallel Processing Approach for Video Data Filtering Using NLP and Object Detection
Bibliographic record
Abstract
In scenarios where users need to extract specific information from large video datasets, an efficient system is essential to filter the relevant segments. This helps in enhancing the overall search and retrieval experience. This paper presents a technique designed to manage large volumes of video data by efficiently identifying and extracting user-preferred content based on user defined criteria. The proposed system uses Natural Language Processing (NLP) to filter audio content and machine learning-based object detection to filter video content. This enables precise extraction based on both spoken dialogue and visual elements. The proposed system also addresses the challenge of time delays associated with analyzing large video datasets by employing advanced filtering methods that utilize parallel processing. This approach reduces the data volume thereby shortening the time required for users to locate specific information. The performance of the technique was assessed through a series of experiments conducted on a large video dataset. The experimental results demonstrate the effectiveness of the system in improving search efficiency within huge volumes of video data.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".