Project Related Entities Tracking on Construction Sites by Particle Filtering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it