ChAirGest

Ruffieux, Simon; Lalanne, Denis; Mugellini, Elena

doi:10.1145/2522848.2532590

Cited by 40 publications

(1 citation statement)

References 22 publications

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“…This study measures segmentation performance using Perf and F1r performance metrics. Perf [27] combines F1-Score and Accurate Temporal Segmentation Rate (ATSR). Perf provides an analysis of the accuracy of the location of key frames at the frame level and also considers the Precision (P) and Recall (R) used in calculating the F1-Score.…”

Section: Performance Metricmentioning

confidence: 99%

Temporal Action Segmentation in Sign Language System for Bahasa Indonesia (SIBI) Videos Using Optical Flow-Based Approach

I Dewa Made Bayu Atmaja Darmawan,

Linawati,

Sukadarmika

et al. 2024

Jurnal Ilmu Komputer dan Informasi

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Sign language (SL) is vital in fostering communication for the deaf and hard-of-hearing communities. Continuous Sign Language Translation (CSLT) is a work that translates sign language into spoken language. CSLT translation is done by changing continuous forms into isolated signs. Segmenting morpheme signs from phrase signs has several challenges, such as the availability of annotated datasets and the complexity of continuous gesture movements. The Indonesian Sign Language (SIBI) system follows Indonesian grammatical norms, including word formation, in contrast to other sign languages with rules derived from their spoken language. In SIBI, a word can consist of a root word and an affix word. Therefore, temporal action segmentation in SIBI is important to reconstruct the results of translating each sign into spoken Indonesian sentences. This research uses an optical flow approach to segment temporal actions in SIBI videos. Optical flow methods that calculate changes in intensity between adjacent frames can be used to determine the occurrence of sign movement or vice versa to determine the delay between sign movements. The absence of intensity differences between the two frames indicates the boundary between sign gestures. This study tested the use of dense optical flow on videos containing SIBI sentences taken from 3 signers. Evaluation is done on several parameters in the dense optical flow algorithm, such as threshold size, PyrScale, and WinSize, to obtain the best accuracy. This paper shows that the optical flow algorithm successfully performs segmentation, as measured by Perf and F1r. The experimental results showed that the highest Perf and F1r yields were 0.8298 and 0.8524, respectively.

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Section: Performance Metricmentioning

confidence: 99%

Temporal Action Segmentation in Sign Language System for Bahasa Indonesia (SIBI) Videos Using Optical Flow-Based Approach

I Dewa Made Bayu Atmaja Darmawan,

Linawati,

Sukadarmika

et al. 2024

Jurnal Ilmu Komputer dan Informasi

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Literature review of vision‐based dynamic gesture recognition using deep learning techniques

Jain

Karsh

Barbhuiya

2022

Concurrency and Computation

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Summary Gesture recognition is the foremost need in building intelligent human‐computer interaction systems to solve many day‐to‐day problems and simplify human life in this digital world. The traditional machine learning (ML) algorithm tried to capture specific handcrafted features, failed miserably in some real‐world environments. Deep learning (DL) techniques have become a sensation among researchers in recent years, making the traditional ML approaches quite obsolete. However, existing reviews consider only a few datasets on which DL algorithm has been applied, and the categorization of the DL algorithms is vague in their review. This study provides the precise categorization of DL algorithms and considers around 15 gesture datasets on which these techniques have been applied. This study also provides a brief overview of the numerous challenging dataset available among the research community and insight into various challenges and limitations of a DL algorithm in vision‐based dynamic gesture recognition.

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