Silhouette-Based On-Site Human Action Recognition in Single-View Video
Bibliographic record
Abstract
On-site worker observation is a fundamental task for a wide spectrum of construction applications such as safety behavior monitoring and productivity analysis. Vision-based action recognition techniques have been proposed to complement the time-consuming and labor-intensive tasks involved in manual observation. In construction, however, previous studies have mainly utilized an RGB-D sensor (e.g., Microsoft Kinect), the operating conditions of which (e.g., active ranges from 80 cm to 4 m, sensitivity to sun light) may hinder the application to actual construction jobsites. To address these issues, we propose a silhouette-based human action recognition method using a single video camera that has less operational constraint. In this framework, the human worker is localized and tracked throughout the monocular video, based on both spatial (i.e., contour of worker) and temporal changes (i.e., moving direction and speed over consecutive frames). Then human action models are learned with temporally adjacent frames and utilized to recognize similar actions in testing video by computing the similarity between the learned action model and newly computed model in a testing dataset. For performance evaluation, we carried out lab experiments, in which a video camera was installed 5–10 m from multiple human subjects. Results indicate that the proposed framework performs well (i.e., an accuracy of 90.68%) to capture predefined poses (e.g., walking, lifting, crawling) in image sequences. This study thus explores an automated means for worker monitoring which potentially helps understand and measure human motions without significant human effort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".