Two-Dimensional Visual Tracking in Construction Scenarios: A Comparative Study
Bibliographic record
Abstract
The tracking of construction resources (e.g., workforce and equipment) in videos, i.e., two-dimensional (2D) visual tracking, has gained significant interest in construction industries. Many studies have relied on 2D visual tracking methods to support the surveillance of construction productivity, safety, and project progress. However, few efforts have been aimed at evaluating the accuracy and robustness of these tracking methods in construction scenarios. The main objective of this research is to fill that knowledge gap. Compared with previous work, a total of 15 2D visual tracking methods were selected here because of their excellent performances identified in the computer vision field. Then, the methods were tested with 20 videos captured from multiple construction jobsites during both day and night. The videos contain construction resources, including but not limited to excavators, backhoes, and compactors. Also, they are characterized by attributes such as occlusions, scale variation, and background clutter. The tracking results were evaluated with the sequence overlap score, center error ratio, and tracking length ratio, respectively. The results indicated that (1) the methods built on local sparse representation were more effective, and (2) the generative tracking strategy typically outperformed the discriminative one when being adopted to track the equipment and workforce in construction scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".