Double-handed dynamic gesture recognition using contour-based hand tracking and maximum mean probability ensembling (MMPE) for Indian Sign Language

Sruthi, C. J.; Lijiya, A

doi:10.1007/s00371-022-02720-x

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…Tracking accuracy related to segmentation accuracy 2022Sruthi [16] Tracking centroid of images with area greater than threshold Centroid Limited applicability, suitable for specific issues only 2022Yadav [17] KLT tracker extracts feature points to obtain centroids Centroid Can only identify consistent gesture trajectories 2022Sarma [9] Particle filter combined with mean shift and CAMShift for centroid tracking Centroid Tracking accuracy related to segmentation accuracy 2023Lu [18] Particle filter and optical flow algorithm for feature point tracking…”

Section: Feature Pointsmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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With the emergence of deep learning methodologies, vision-based gesture recognition technology has continuously advanced. This paper primarily delves into four main stages of vision-based gesture recognition: gesture segmentation, gesture tracking, feature extraction, and gesture classification. It sequentially introduces pertinent techniques from representative literature spanning from 2018 to 2023. Based on this analysis, the current status of vision-based gesture recognition technology is examined, paving the way for predicting its future trends and developments.

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