Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition

Abstract

Sign language recognition using computational models is a challenging problem that requires simultaneous spatio-temporal modeling of the multiple sources, i.e. faces, hands, body, etc. In this paper, we propose an isolated sign language recognition model based on a model trained using Motion History Images (MHI) that are generated from RGB video frames. RGB-MHI images represent spatio-temporal summary of each sign video effectively in a single RGB image. We propose two different approaches using this RGB-MHI model. In the first approach, we use the RGB-MHI model as a motion-based spatial attention module integrated into a 3D-CNN architecture. In the second approach, we use RGB-MHI model features directly with the features of a 3D-CNN model using a late fusion technique. We perform extensive experiments on two recently released large-scale isolated sign language datasets, namely AUTSL and BosphorusSign22k. Our experiments show that our models, which use only RGB data, can compete with the state-of-the-art models in the literature that use multi-modal data.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• The Kinetics Human Action Video Dataset
• AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods
• Quantitative Survey of the State of the Art in Sign Language Recognition
• Global-local Enhancement Network for NMFs-aware Sign Language Recognition
• BosphorusSign22k Sign Language Recognition Dataset

Cited by in corpus (3)

• Survey on Hand Gesture Recognition from Visual Input
• E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods
• Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks