Bibliographic record
Abstract
Video monitoring of traffic is a common practice in major cities. The data generated by video monitoring has practical uses such as traffic analysis for city planning. However, the usefulness of video monitoring of traffic is limited unless there is also a reliable way to automatically classify road users. This thesis presents a framework that is designed to classify road users into vehicles, cyclists and pedestrians by using the motion cues obtained from their tracks. The road user tracks are obtained using a tracker system such as computer vision techniques. As such, this classification technique does not require additional video or image analysis alongside obtained road user tracks. The separate pieces of information are gained from these motion cues are hereafter called Classifiers. There are nineteen classifiers included in this framework. These classifiers include: average and maximum speeds; average and maximum acceleration; average and maximum deceleration; average and maximum direction; average of change in direction; average of cosine of change in direction; average and maximum area; average and maximum length; average and maximum width; peaks in speed; effective frequency; and the effective weighted average frequency. After obtaining the classifiers' values from the tracked objects' tracks, the information from these classifiers will be assessed and integrated using fuzzy membership approach, which in turn requires prior configurations to be available. This will lead to the final classification of the tracked object. The performance of this framework demonstrated very promising results under different measures. An important contribution of this study is the creation of a robust approach that can integrate different motion cues using fuzzy membership framework. The developed approach also uses parametric classifiers, which do not II depend on the geometry or specific traffic operation of the intersection. This is a key advantage because it enables transferability and improves the practicality and usefulness of the approach. III
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".