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Related Experiment Video

Updated: Sep 15, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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A non-anatomical graph structure for boundary detection in continuous sign language.

Razieh Rastgoo1, Kourosh Kiani2, Sergio Escalera3

  • 1Electrical and Computer Engineering Department, Semnan University, Semnan, 3513119111, Iran. rrastgoo@semnan.ac.ir.

Scientific Reports
|July 15, 2025
PubMed
Summary

This study introduces a deep learning model combining Graph Convolutional Networks (GCN) and Transformer models for precise sign language boundary detection in continuous videos. The approach enhances sign recognition by analyzing hand joint movements and temporal information.

Keywords:
Boundary detectionContinuous sign sequenceGraph convolutional network (GCN)Isolated sign recognitionTransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Boundary detection of isolated signs in continuous sign language videos presents a significant challenge.
  • Existing methods often rely on handcrafted features, limiting performance and adaptability.
  • Incorporating hand structure and dynamics is crucial for accurate sign recognition.

Purpose of the Study:

  • To propose a novel deep learning approach for accurate boundary detection of isolated signs within continuous sign language videos.
  • To enhance model performance by replacing handcrafted feature extractors with a GCN-Transformer architecture.
  • To effectively utilize hand structure and movement dynamics for improved sign boundary detection.

Main Methods:

  • A two-step approach: pre-training on isolated sign videos and deployment on continuous sign videos.
  • Utilizing Graph Convolutional Networks (GCN) for enriched spatial feature extraction and Transformer models for temporal information processing.
  • Introducing a non-anatomical graph structure to represent hand joint movements and relations, coupled with a sliding window mechanism and a post-processing module for final boundary detection.

Main Results:

  • The proposed GCN-Transformer model demonstrates superior performance in detecting isolated sign boundaries within continuous sequences.
  • Experimental results on two datasets validate the model's effectiveness in tackling the complexities of sign language boundary detection.
  • The non-anatomical hand graph structure and self-attention mechanism contribute significantly to the model's success.

Conclusions:

  • The developed deep learning approach effectively addresses the challenge of isolated sign boundary detection in continuous sign language videos.
  • The combination of GCN, Transformer, and a novel hand graph structure offers a robust solution for sign language recognition.
  • This work advances the field by providing a more accurate and adaptable method for analyzing sign language videos.