Development of Bicycle and Pedestrian Detection and Classification Algorithm for Active-Infrared Overhead Vehicle Imaging Sensors
Bibliographic record
Abstract
Existing algorithms used with active-infrared overhead vehicle-imaging sensors consider vehicle size and speed attributes as basic parameters to detect and classify 11 categories of motorized vehicles. These algorithms could not detect and classify bicycles and pedestrians. This research focused on developing and evaluating algorithms for active-infrared overhead vehicle-imaging sensor technology to detect and classify non-motorized users. Development of the theory and algorithm used to automate the simultaneous detection and classification of bicycles and pedestrians along with the field investigations to evaluate its effectiveness are described. The new algorithm used the concept of message sequencing to incorporate existing active-infrared technology theory. Bicycles and pedestrians intersected the infrared scan patterns in different sequences that, along with the infrared images, provided unique detection and classification identification. The algorithm was integrated within the existing active-infrared technology, and a field evaluation was conducted on bicycle and pedestrian trails. The algorithm created an intelligent technology to detect and classify bicycles and pedestrians. Nearly 100% of bicycles and pedestrians were detected, and about 92% of them were successfully classified. Automated data collection technology can be useful in obtaining more comprehensive travel data and in forecasting demand for design and policy making related to nonmotorized transportation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".