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Record W2576485891 · doi:10.1109/mmsp.2016.7813380

Human gesture recognition via bag of angles for 3D virtual city planning in CAVE environment

2016· article· en· W2576485891 on OpenAlexaff
Nour El Din Elmadany, Yifeng He, Ling Guan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHuman Pose and Action Recognition
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGestureComputer scienceRepresentation (politics)Gesture recognitionCaveComputer visionArtificial intelligenceHuman skeletonVirtual realitySkeleton (computer programming)Wearable computerHuman–computer interactionPattern recognition (psychology)Geography

Abstract

fetched live from OpenAlex

Cave Automatic Virtual Environment (CAVE) provides an immersive virtual environment for 3D city planning. However, the user in the cave has to wear wearable markers for the interaction with the 3D models. To develop a natural interaction, depth camera like Kinect has to be considered. In this paper, we propose a new skeleton joint representation called Bag of Angles (BoA) for human gesture recognition. We evaluated our proposed BoA representation on two dataset UTD-MHAD and UTD-MHAD-KinectV2. The evaluation results demonstrated that the proposed BoA representation can achieve a higher recognition accuracy compared to the other existing representation methods.

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 score0.902
Threshold uncertainty score0.235

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 communication0.0000.000
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 head0.039
GPT teacher head0.258
Teacher spread0.219 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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