Project Related Entities Tracking on Construction Sites by Particle Filtering
Bibliographic record
Abstract
Vision-based tracking for project related entities has attracted practitioners’ interests and attentions; it can provide beneficial data for productivity analysis and safety monitoring. Some studies on tracking workforce and equipment using video cameras placed onsite have proved the feasibility and efficiency of vision-based tracking methods. However, existing tracking techniques have difficulties in tracking objects when occlusions occur. This paper presents a visual tracking method based on particle filters to resolve this issue. The method includes two main stages, prediction, and update. Initially, the target object is located with a rectangular window, and particles are generated from a normal distribution. Then, particles are propagated, and the weight of each particle is determined by the observation likelihoods. Particles are resampled to localize the target object based on weights. In this way, the personnel or construction equipment can be traced. The jobsite of Roccabella residential project in Montreal was selected as the test bed. A high definition camera was placed onsite to record the construction activities. Then, the collected videos were used to evaluate the tracking performance of this method. The results indicated the method was effective to track the object of interest in the complex situation of occlusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".