Egocentric-video-based construction quality supervision (EgoConQS): Application of automatic key activity queries
Bibliographic record
Abstract
Construction quality supervision is essential for project success and safety. Traditional methods relying on manual inspections and paper records are time-consuming, error-prone, and difficult to verify. In-process construction quality supervision offers a more direct and effective approach. Recent advancements in computer vision and egocentric video analysis present opportunities to enhance these processes. This paper introduces the use of key activity queries on egocentric video data for construction quality supervision. A framework, Egocentric Video-Based Construction Quality Supervision (EgoConQS), is developed using a video self-stitching graph network to identify key activities in egocentric videos. EgoConQS facilitates efficient monitoring and quick review of key activity frames. Empirical evaluation with real-world data demonstrates an average recall of 35.85 % and a mAP score of 6.07 %, highlighting the potential of key activity queries for reliable and convenient quality supervision. • Proposed key activity queries on egocentric video data for construction quality supervision. • Introduced a video self-stitching graph network approach to obtain key activities. • Proposed activity taxonomy and annotation for construction quality supervision. • Experimentally confirmed the efficacy of the framework on real-world construction 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".