Video Analysis Tool with Template Matching and Audio-Track Processing
Bibliographic record
Abstract
In the last few decades, we have observed the rapid advancement of multimedia analysis tools, and video analysis is one of such application domains. While much effort has been put into the analysis of business and professional videos (e.g., films, professional sports, security cameras) by utilizing advanced image processing algorithms, many of these approaches often do not work well with raw videos that are recorded with a single, consumer-level camera (e.g., a mobile phone) by non-professional videographers. These "amateur" videos typically do not have multiple view-angles and often contain low-resolution and noisy images, making it more difficult to apply certain algorithms compared to cases with videos that are professionally recorded with multiple high-quality cameras and that are properly edited. In this paper, we discuss a prototype interactive video image analysis tool that combines both the image and audio analysis of such videos. The tool provides multiple channels of data analysis visualizations that presumably complement each other for the users to understand the video content effectively and more easily.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".