Development of an integrated system based vehicle tracking algorithm with shadow removal and occlusion handling methods
Bibliographic record
Abstract
Abstract Video image processing system (VIPS) is more efficient than other detecting systems. However, VIPS involves outdoor images and is very sensitive to the external environment, which could greatly decrease its accuracy according to rapid environmental changes. To obtain accurate traffic data accordingly, VIPS must address the problems such as growing shadows in transition; distortion of images due to the headlights at night; noises caused by the rain, snow or fog; and occlusions. This study intends to accurately calculate traffic data while addressing the shadow and occlusion problems, which are the most difficult tasks for the image‐detector‐based traffic data system. In this study, an algorithm for the individual vehicle tracking collection was developed to address the occlusion problem and to eliminate the noises or shadows caused by external environmental factors. A traffic data collection system was also proposed in order to accurately track individual vehicles that pass through the detection region. In addition, establishing an integrated system with shadow removal and occlusion handling using an image processing was also proposed. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".