Automated Visual Recognition of Dump Trucks in Construction Videos
Bibliographic record
Abstract
Earthmoving plants are essential but costly resources in the construction of heavy civil engineering projects. In addition to proper allocation, ongoing control of this equipment is necessary to ensure and increase the productivity of earthmoving operations. Captured videos from construction sites are potential tools to control earthmoving operations; however, the current practice of manual data extraction from surveillance videos is tedious, costly, and error prone. Cutting-edge computer vision techniques have the potential to automate equipment monitoring tasks. This paper presents research in the evaluation of combinations of existing object recognition and background subtraction algorithms to recognize off-highway dump trucks in noisy video streams containing other active machines. Two detection algorithms, namely, Haar–histogram of oriented gradients (HOG) and Blob-HOG, are presented and evaluated for their ability to recognize dump trucks in videos as measured by both effectiveness and timeliness. The results of this study can help practitioners select a suitable approach to recognize such equipment in videos for real-time applications such as productivity measurement, performance control, and proactive work-zone safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".