A Mixed Urban Traffic Road-Users Classification Based on Automated Video Data Analysis
Bibliographic record
Abstract
This article describes a novel approach for the classification of modes of travel in mixed traffic intersections based on video data. The classification is semi-supervised where the selected classification features included road-user movements’ characteristics such as speed, gait and cadence frequencies (for pedestrians and cyclists respectively) in addition to the estimate of the road-user occupied area. A modified version of the semi-supervised spectral clustering method is adapted where the selected labeled features identify possible relations; thereby enforcing certain constraints between features. Two case studies are demonstrated with video data collected at a roundabout in Vancouver, Canada and a U-turn crossover in Cairo, Egypt. Road-users were first detected and tracked using object recognition methods. The classification algorithm was then applied on the extracted objects trajectories to identify the corresponding modes of travel. Experiments were conducted on the two case studies along with a comparison to other related classification methods. A sensitivity analysis was undertaken to assess the impact of the constraints selection on the effectiveness of the method. A performance analysis demonstrated the robustness of the proposed classification method with an accuracy of higher than 87 percent achieved for both datasets. The experimental results showed that the method also outperformed other related classification methods. This research contributes to the literature of automated data collection and analysis of non-motorized traffic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".