Joint Motion Affinity Maps (JMAM) and Their Impact on Deep Learning Models for 3D Sign Language Recognition

Joint Motion Affinity Maps (JMAM) and Their Impact on Deep Learning Models for 3D Sign Language Recognition

Previous works on 3D joint based feature representations of the human body as colour coded images (maps) were developed based on the joint positions, distances and angles or a combination of them for applications such as human action (sign language) recognition. These 3D joint maps have shown to sin...

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Bibliographic Details
Main Authors: P. V. V. Kishore, D. Anil Kumar, Rama Chaithanya Tanguturi, K. Srinivasarao, P. Praveen Kumar, D. Srihari
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
3D joint data processing
motion affinity maps
radial basis kernel
neural network with self-attention
Online Access:https://ieeexplore.ieee.org/document/10401167/

Internet

https://ieeexplore.ieee.org/document/10401167/

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