Semantic Annotation of Videos from Equipment-Intensive Construction Operations by Shot Recognition and Probabilistic Reasoning
Bibliographic record
Abstract
Digital videotaping of operations at construction sites has proven to be a useful resource in the industry. These valuable visual resources, however, are not used to their full potential because they are manually archived and the contents of the videos are not efficiently annotated. A number of research efforts have investigated computer vision algorithms to detect and track construction resources in videos that are mostly captured by stationary cameras; however, only limited research has been performed on semantic annotation of the videos. This paper presents an automated system to analyze the content of heavy-construction videos, including videos with moving viewpoints, and to index them based on midlevel (i.e., objects) and high-level (i.e., operations) semantic content. This system divides videos based on the transition of shots, and then analyzes the objects that appear in each scene. Afterward, it uses a probabilistic method to annotate videos. The experimental results showed promising performance of the developed framework and also highlighted possibilities for the future research direction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".