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Record W2159345275 · doi:10.1109/wiamis.2009.5031441

Motion-swarm widgets for video interaction

2009· article· en· W2159345275 on OpenAlexaff
Jeffrey E. Boyd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHuman Pose and Action Recognition
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceMotion (physics)GestureComputer visionArtificial intelligenceVideo trackingField (mathematics)SegmentationTracking (education)Object (grammar)Human–computer interactionMatch movingPosition (finance)Gesture recognitionParticle swarm optimizationMultimediaMachine learning

Abstract

fetched live from OpenAlex

Computer vision systems for human-computer interaction have tended towards more precise forms of interface that require complex vision tasks such as segmentation, tracking, object recognition, pose estimation, and gesture recognition. We present an alternate approach that extrapolates a method for enmasse audience interaction through video. The enmasse interaction simulates a particle moving in the field of motion created by the audience, and the audience interacts by manipulating the particle position. In this paper, we show that by adding sets of constraints to the particle motion, one can build GUI-style widgets. We describe several of these widgets and the results of a small-sample pilot study to test them. The results are not conclusive, but are encouraging, suggesting possibilities for video games and interactive theatre.

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.

How this classification was reachedexpand

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)0.000
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 score0.965
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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