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Record W2156994206 · doi:10.1109/itsc.2002.1041181

Mobile vision-based vehicle tracking and traffic control

2003· article· en· W2156994206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Surveillance and Tracking Methods
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsActive visionComputer scienceFocus (optics)Machine visionZoomMobile robotTask (project management)Orientation (vector space)Artificial intelligenceSmart cameraComputer visionHuman–computer interactionEngineeringRobotSystems engineering

Abstract

fetched live from OpenAlex

This paper discusses work-in-progress to develop a mobile, bus-mounted machine vision system for transit and traffic monitoring in urban corridors, as required by Intelligent Transportation Systems. In contrast to earlier machine vision technologies used for traffic management, which mainly rely on simple algorithms to detect certain traffic characteristics, the new proposed approach makes use of a recent trend in computer vision research: namely the active vision paradigm. Active vision systems have mechanisms that can actively control camera parameters, such as orientation, focus, zoom, and convergence, in response to the requirements of the task and external stimuli. Mounting active vision systems on buses will have the advantage of providing real-time feedback of the current traffic conditions while possessing the intelligence and visual skills which allow them to interact with a rapidly changing dynamic environment such as moving traffic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.281
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations22
Published2003
Admission routes1
Has abstractyes

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